{"url":"/task/image-classification","name":"Image Classification","slug":"image-classification","description_markdown":"**Image Classification** is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike [object detection](/task/object-detection), which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as [image retrieval](/task/image-retrieval), which also involves finding similar images in a large database.\r\n\r\n\r\n<span class=\"description-source\">Source: [Metamorphic Testing for Object Detection Systems ](https://arxiv.org/abs/1912.12162)</span>","categories":[{"name":"Adversarial","url":"/area/adversarial"},{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":10488,"papers_with_code":4702,"benchmarks":166,"benchmark_tables_in_archive":177,"benchmark_tables_shown":177,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":283,"subtasks":33,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/image-classification-on-imagenet","slug":"image-classification-on-imagenet","dataset":"ImageNet","dataset_url":"/dataset/imagenet","rows_in_archive":1060,"metrics":["Top 1 Accuracy","Number of params","GFLOPs","Hardware Burden","Top 5 Accuracy","Operations per network pass"],"first_row_in_archive_order":{"model":"CoCa (finetuned)","paper_title":"CoCa: Contrastive Captioners are Image-Text Foundation Models","paper_url":"/paper/coca-contrastive-captioners-are-image-text","paper_date":"2022-05-04","arxiv_id":"2205.01917","code_links":[{"title":"mlfoundations/open_clip","url":"https://github.com/mlfoundations/open_clip"},{"title":"facebookresearch/multimodal","url":"https://github.com/facebookresearch/multimodal"},{"title":"lucidrains/CoCa-pytorch","url":"https://github.com/lucidrains/CoCa-pytorch"},{"title":"amitakamath/whatsup_vlms","url":"https://github.com/amitakamath/whatsup_vlms"},{"title":"amitakamath/hard_positives","url":"https://github.com/amitakamath/hard_positives"},{"title":"Chaolei98/FreeZAD","url":"https://github.com/Chaolei98/FreeZAD"}],"syntology":{"n":17,"n_ran":9,"n_unverified":8,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-cifar-10","slug":"image-classification-on-cifar-10","dataset":"CIFAR-10","dataset_url":"/dataset/cifar-10","rows_in_archive":265,"metrics":["Percentage correct","Top-1 Accuracy","Accuracy","Parameters","Top 1 Accuracy","F1","Cross Entropy Loss"],"first_row_in_archive_order":{"model":"ViT-H/14","paper_title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","paper_url":"/paper/an-image-is-worth-16x16-words-transformers-1","paper_date":"2020-10-22","arxiv_id":"2010.11929","code_links":[{"title":"huggingface/transformers","url":"https://github.com/huggingface/transformers"},{"title":"labmlai/annotated_deep_learning_paper_implementations","url":"https://github.com/labmlai/annotated_deep_learning_paper_implementations"},{"title":"rwightman/pytorch-image-models","url":"https://github.com/rwightman/pytorch-image-models"},{"title":"lucidrains/vit-pytorch","url":"https://github.com/lucidrains/vit-pytorch"},{"title":"pytorch/vision","url":"https://github.com/pytorch/vision/blob/main/torchvision/models/vision_transformer.py"},{"title":"lukas-blecher/LaTeX-OCR","url":"https://github.com/lukas-blecher/LaTeX-OCR"},{"title":"google-research/vision_transformer","url":"https://github.com/google-research/vision_transformer"},{"title":"kornia/kornia","url":"https://github.com/kornia/kornia"},{"title":"PaddlePaddle/PaddleClas","url":"https://github.com/PaddlePaddle/PaddleClas"},{"title":"open-mmlab/mmclassification","url":"https://github.com/open-mmlab/mmclassification"},{"title":"towhee-io/towhee","url":"https://github.com/towhee-io/towhee"},{"title":"facebookresearch/vissl","url":"https://github.com/facebookresearch/vissl"},{"title":"keras-team/keras-io","url":"https://github.com/keras-team/keras-io/blob/master/examples/vision/image_classification_with_vision_transformer.py"},{"title":"jeonsworld/ViT-pytorch","url":"https://github.com/jeonsworld/ViT-pytorch"},{"title":"alibaba/EasyCV","url":"https://github.com/alibaba/EasyCV"},{"title":"facebookresearch/ClassyVision","url":"https://github.com/facebookresearch/ClassyVision/tree/master/examples/vit"},{"title":"davisking/dlib-models","url":"https://github.com/davisking/dlib-models"},{"title":"BR-IDL/PaddleViT","url":"https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/ViT"},{"title":"kakaobrain/coyo-dataset","url":"https://github.com/kakaobrain/coyo-dataset"},{"title":"The-AI-Summer/self_attention","url":"https://github.com/The-AI-Summer/self_attention"},{"title":"facebookresearch/hiera","url":"https://github.com/facebookresearch/hiera"},{"title":"jacobgil/vit-explain","url":"https://github.com/jacobgil/vit-explain"},{"title":"OML-Team/open-metric-learning","url":"https://github.com/OML-Team/open-metric-learning"},{"title":"lukemelas/PyTorch-Pretrained-ViT","url":"https://github.com/lukemelas/PyTorch-Pretrained-ViT"},{"title":"keras-team/keras-cv","url":"https://github.com/keras-team/keras-cv/blob/master/keras_cv/models/vit.py"},{"title":"Westlake-AI/openmixup","url":"https://github.com/Westlake-AI/openmixup"},{"title":"mahmoodlab/hipt","url":"https://github.com/mahmoodlab/hipt"},{"title":"SHI-Labs/Compact-Transformers","url":"https://github.com/SHI-Labs/Compact-Transformers"},{"title":"wangguanan/light-reid","url":"https://github.com/wangguanan/light-reid"},{"title":"TACJu/TransFG","url":"https://github.com/TACJu/TransFG"},{"title":"nasa-impact/hls-foundation-os","url":"https://github.com/nasa-impact/hls-foundation-os"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/blob/master/research/cv/vit_base/"},{"title":"asyml/vision-transformer-pytorch","url":"https://github.com/asyml/vision-transformer-pytorch"},{"title":"haiyang-w/git","url":"https://github.com/haiyang-w/git"},{"title":"faustomorales/vit-keras","url":"https://github.com/faustomorales/vit-keras"},{"title":"jankrepl/mildlyoverfitted","url":"https://github.com/jankrepl/mildlyoverfitted"},{"title":"gupta-abhay/ViT","url":"https://github.com/gupta-abhay/ViT"},{"title":"martinsbruveris/tensorflow-image-models","url":"https://github.com/martinsbruveris/tensorflow-image-models"},{"title":"staghado/vit.cpp","url":"https://github.com/staghado/vit.cpp"},{"title":"PaddlePaddle/PASSL","url":"https://github.com/PaddlePaddle/PASSL"},{"title":"mindspore-ecosystem/mindcv","url":"https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/vit.py"},{"title":"BrianPulfer/PapersReimplementations","url":"https://github.com/BrianPulfer/PapersReimplementations"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/official/cv/vit"},{"title":"emla2805/vision-transformer","url":"https://github.com/emla2805/vision-transformer"},{"title":"Kevinz-code/CSRA","url":"https://github.com/Kevinz-code/CSRA"},{"title":"Mayurji/Image-Classification-PyTorch","url":"https://github.com/Mayurji/Image-Classification-PyTorch"},{"title":"tintn/vision-transformer-from-scratch","url":"https://github.com/tintn/vision-transformer-from-scratch"},{"title":"SforAiDl/vformer","url":"https://github.com/SforAiDl/vformer"},{"title":"PaddlePaddle/PLSC","url":"https://github.com/PaddlePaddle/PLSC/tree/master/task/classification/vit"},{"title":"s-chh/pytorch-scratch-vision-transformer-vit","url":"https://github.com/s-chh/pytorch-scratch-vision-transformer-vit"},{"title":"junyongyou/triq","url":"https://github.com/junyongyou/triq"},{"title":"ra1ph2/Vision-Transformer","url":"https://github.com/ra1ph2/Vision-Transformer"},{"title":"asarigun/TransGAN","url":"https://github.com/asarigun/TransGAN"},{"title":"ashishpatel26/Vision-Transformer-Keras-Tensorflow-Pytorch-Examples","url":"https://github.com/ashishpatel26/Vision-Transformer-Keras-Tensorflow-Pytorch-Examples"},{"title":"kamalkraj/Vision-Transformer","url":"https://github.com/kamalkraj/Vision-Transformer"},{"title":"tuvovan/Vision_Transformer_Keras","url":"https://github.com/tuvovan/Vision_Transformer_Keras"},{"title":"tahmid0007/VisionTransformer","url":"https://github.com/tahmid0007/VisionTransformer"},{"title":"KatherLab/HIA","url":"https://github.com/KatherLab/HIA"},{"title":"naver-ai/pflayer","url":"https://github.com/naver-ai/pflayer"},{"title":"purbayankar/Hyperspectral-Vision-Transformer","url":"https://github.com/purbayankar/Hyperspectral-Vision-Transformer"},{"title":"UdbhavPrasad072300/Transformer-Implementations","url":"https://github.com/UdbhavPrasad072300/Transformer-Implementations"},{"title":"UdbhavPrasad072300/Transformer-Implementation-and-Language-Translation","url":"https://github.com/UdbhavPrasad072300/Transformer-Implementation-and-Language-Translation"},{"title":"UdbhavPrasad072300/Transformer-Implementation","url":"https://github.com/UdbhavPrasad072300/Transformer-Implementation"},{"title":"jiangtaoxie/So-ViT","url":"https://github.com/jiangtaoxie/So-ViT"},{"title":"james77777778/keras-image-models","url":"https://github.com/james77777778/keras-image-models"},{"title":"mashaan14/VisionTransformer-MNIST","url":"https://github.com/mashaan14/VisionTransformer-MNIST"},{"title":"BebDong/MXNetSeg","url":"https://github.com/BebDong/MXNetSeg"},{"title":"DavidLandup0/deepvision","url":"https://github.com/DavidLandup0/deepvision"},{"title":"DominikBatic/EndoViT","url":"https://github.com/DominikBatic/EndoViT"},{"title":"HzcIrving/DeepLearning_PlayGround","url":"https://github.com/HzcIrving/DeepLearning_PlayGround/tree/main/VIT"},{"title":"ruiqirichard/eegeyenet-vit","url":"https://github.com/ruiqirichard/eegeyenet-vit"},{"title":"megvii-research/basecls","url":"https://github.com/megvii-research/basecls/tree/main/zoo/public/vit"},{"title":"04RR/SOTA-Vision","url":"https://github.com/04RR/SOTA-Vision"},{"title":"protonx-engineering/vit","url":"https://github.com/protonx-engineering/vit"},{"title":"TheTensorDude/vision_transformer_tf","url":"https://github.com/TheTensorDude/vision_transformer_tf"},{"title":"jaketae/mlp-mixer","url":"https://github.com/jaketae/mlp-mixer"},{"title":"uygarkurt/ViT-PyTorch","url":"https://github.com/uygarkurt/ViT-PyTorch"},{"title":"sliao-mi-luku/Galaxy-Zoo-Classification","url":"https://github.com/sliao-mi-luku/Galaxy-Zoo-Classification"},{"title":"soumik12345/Vision-Transformer","url":"https://github.com/soumik12345/Vision-Transformer"},{"title":"smitheric95/MoCoViT-PyTorch","url":"https://github.com/smitheric95/MoCoViT-PyTorch"},{"title":"affjljoo3581/deit3-jax","url":"https://github.com/affjljoo3581/deit3-jax"},{"title":"uzi0espil/research-papers-implementation","url":"https://github.com/uzi0espil/research-papers-implementation/tree/master/Vision%20Transformer"},{"title":"mujiyantosvc/Facial-Expression-Recognition-FER-for-Mental-Health-Detection-","url":"https://github.com/mujiyantosvc/Facial-Expression-Recognition-FER-for-Mental-Health-Detection-"},{"title":"birder/birder","url":"https://gitlab.com/birder/birder"},{"title":"leemsaebom/attention-guided-cam-visual-explanations-of-vision-transformer-guided-by-self-attention","url":"https://github.com/leemsaebom/attention-guided-cam-visual-explanations-of-vision-transformer-guided-by-self-attention"},{"title":"quanmario0311/ViT_PyTorch","url":"https://github.com/quanmario0311/ViT_PyTorch"},{"title":"explainingai-code/VIT-Pytorch","url":"https://github.com/explainingai-code/VIT-Pytorch"},{"title":"jo1jun/Vision_Transformer","url":"https://github.com/jo1jun/Vision_Transformer"},{"title":"xiuyu0000/new_papers_codes","url":"https://github.com/xiuyu0000/new_papers_codes/tree/main/vit"},{"title":"smu-ivpl/DeepfakeDetection","url":"https://github.com/smu-ivpl/DeepfakeDetection"},{"title":"tw-yuhsi/a-new-perspective-for-shuttlecock-hitting-event-detection","url":"https://github.com/tw-yuhsi/a-new-perspective-for-shuttlecock-hitting-event-detection"},{"title":"wish44165/A-New-Perspective-for-Shuttlecock-Hitting-Event-Detection","url":"https://github.com/wish44165/A-New-Perspective-for-Shuttlecock-Hitting-Event-Detection"},{"title":"sayannath/ViT-Image-Classification","url":"https://github.com/sayannath/ViT-Image-Classification"},{"title":"YousefGamal220/Vision-Transformers","url":"https://github.com/YousefGamal220/Vision-Transformers"},{"title":"gmum/dl-mo-2021","url":"https://github.com/gmum/dl-mo-2021"},{"title":"nachiket273/Vision_transformer_pytorch","url":"https://github.com/nachiket273/Vision_transformer_pytorch"},{"title":"Abdulrahman-Adel/Real-Life-Violence-Detection","url":"https://github.com/Abdulrahman-Adel/Real-Life-Violence-Detection"},{"title":"septmars/DL","url":"https://github.com/septmars/DL"},{"title":"mtancak/PyTorch-ViT-Visual-Transformer","url":"https://github.com/mtancak/PyTorch-ViT-Visual-Transformer"},{"title":"zer0sh0t/artificial_intelligence","url":"https://github.com/zer0sh0t/artificial_intelligence/tree/master/vision_models/vision_transformer"},{"title":"mtancak1/PyTorch-ViT-Visual-Transformer","url":"https://github.com/mtancak1/PyTorch-ViT-Visual-Transformer"},{"title":"IMvision12/keras-vision-models","url":"https://github.com/IMvision12/keras-vision-models"},{"title":"xiuyu0000/papers_with_examples","url":"https://github.com/xiuyu0000/papers_with_examples/tree/main/ViT"},{"title":"sneakatyou/ViT-Tensorflow-2.0","url":"https://github.com/sneakatyou/ViT-Tensorflow-2.0"},{"title":"arkel23/PyTorch-Pretrained-ViT","url":"https://github.com/arkel23/PyTorch-Pretrained-ViT"},{"title":"woctezuma/steam-CLIP","url":"https://github.com/woctezuma/steam-CLIP"},{"title":"stevenwalton/scs-cct","url":"https://github.com/stevenwalton/scs-cct"},{"title":"Ugenteraan/Masked-AutoEncoder-PyTorch","url":"https://github.com/Ugenteraan/Masked-AutoEncoder-PyTorch"},{"title":"nateraw/lightning-vision-transformer","url":"https://github.com/nateraw/lightning-vision-transformer"},{"title":"seujung/pytorch-vit","url":"https://github.com/seujung/pytorch-vit"},{"title":"avinash31d/paper-implementations","url":"https://github.com/avinash31d/paper-implementations/tree/main/vit"},{"title":"Oguzhanercan/Vision-Transformers","url":"https://github.com/Oguzhanercan/Vision-Transformers"},{"title":"skchen1993/TrangFG","url":"https://github.com/skchen1993/TrangFG"},{"title":"holdfire/FAS","url":"https://github.com/holdfire/FAS"},{"title":"dispink/xpt","url":"https://github.com/dispink/xpt"},{"title":"holdfire/CLS","url":"https://github.com/holdfire/CLS"},{"title":"AlifAshrafee/ViT-pytorch-for-Cooking-State-Recognition","url":"https://github.com/AlifAshrafee/ViT-pytorch-for-Cooking-State-Recognition"},{"title":"sangHa0411/VIT","url":"https://github.com/sangHa0411/VIT"},{"title":"zpc-666/Paddle-R-Drop","url":"https://github.com/zpc-666/Paddle-R-Drop"},{"title":"Burf/VisionTransformer-Tensorflow2","url":"https://github.com/Burf/VisionTransformer-Tensorflow2"},{"title":"gnoses/ViT_examples","url":"https://github.com/gnoses/ViT_examples"},{"title":"Ugenteraan/Vanilla-ViT","url":"https://github.com/Ugenteraan/Vanilla-ViT"},{"title":"HyeonhoonLee/MAIC2021_Sleep","url":"https://github.com/HyeonhoonLee/MAIC2021_Sleep"},{"title":"ttt496/VisionTransformer","url":"https://github.com/ttt496/VisionTransformer"},{"title":"innat/LearnedResizer-Vision-Transformer","url":"https://github.com/innat/LearnedResizer-Vision-Transformer"},{"title":"Thanusan19/Vision_Transformer","url":"https://github.com/Thanusan19/Vision_Transformer"},{"title":"KiUngSong/Vision","url":"https://github.com/KiUngSong/Vision"},{"title":"liuxingwt/CLS","url":"https://github.com/liuxingwt/CLS"},{"title":"konstantinos-p/image_classification_SOTA","url":"https://github.com/konstantinos-p/image_classification_SOTA"},{"title":"qiaopTDUN/mae-repo","url":"https://github.com/qiaopTDUN/mae-repo"},{"title":"charchit7/Using_Transoformers","url":"https://github.com/charchit7/Using_Transoformers"},{"title":"kingcong/vit","url":"https://github.com/kingcong/vit"},{"title":"nachiket273/VisTrans","url":"https://github.com/nachiket273/VisTrans"},{"title":"alililia/vit_base_GPU","url":"https://github.com/alililia/vit_base_GPU"},{"title":"conceptofmind/ViT-haiku","url":"https://github.com/conceptofmind/ViT-haiku"},{"title":"SrinjaySarkar/ViT","url":"https://github.com/SrinjaySarkar/ViT"},{"title":"nima1999nikkhah/ViT-Hybrid","url":"https://github.com/nima1999nikkhah/ViT-Hybrid"},{"title":"shahrukhx01/ocr-test","url":"https://github.com/shahrukhx01/ocr-test"},{"title":"ahmed-alllam/Equinox","url":"https://github.com/ahmed-alllam/Equinox/blob/main/examples/vision_transformer.ipynb"},{"title":"ludics/ViT-Retri","url":"https://github.com/ludics/ViT-Retri"},{"title":"Mind23-2/MindCode-1","url":"https://github.com/Mind23-2/MindCode-1"},{"title":"Mind23-2/MindCode-89","url":"https://github.com/Mind23-2/MindCode-89"},{"title":"ZhouDaShan123/vit","url":"https://github.com/ZhouDaShan123/vit"},{"title":"rayanramoul/Visual-Transformer-PyTorch","url":"https://github.com/rayanramoul/Visual-Transformer-PyTorch"},{"title":"alililia/vit_base_Ascend","url":"https://github.com/alililia/vit_base_Ascend"},{"title":"gimme1dollar/vision-transformer","url":"https://github.com/gimme1dollar/vision-transformer"},{"title":"timH6502/VisionTransformer-PyTorch","url":"https://github.com/timH6502/VisionTransformer-PyTorch"},{"title":"modeeric/eegvit-tcnet","url":"https://github.com/modeeric/eegvit-tcnet"},{"title":"drumpt/ViT","url":"https://github.com/drumpt/ViT"},{"title":"BaiqiangGit/15minCode","url":"https://github.com/BaiqiangGit/15minCode"},{"title":"bshantam97/Attention_Based_Networks","url":"https://github.com/bshantam97/Attention_Based_Networks"},{"title":"YanYan0716/vision_transform","url":"https://github.com/YanYan0716/vision_transform"},{"title":"mdmhriday/vision-transformers","url":"https://github.com/mdmhriday/vision-transformers"},{"title":"SupreethRao99/VisionTransformer","url":"https://github.com/SupreethRao99/VisionTransformer"},{"title":"MindSpore-scientific/code-7","url":"https://github.com/MindSpore-scientific/code-7/tree/main/VisionTransformer"},{"title":"meowbutlerdev/ViT","url":"https://github.com/meowbutlerdev/ViT"},{"title":"Aedelon/ViT-PyTorch-Replication","url":"https://github.com/Aedelon/ViT-PyTorch-Replication"},{"title":"Julien-pour/music_classifcation","url":"https://github.com/Julien-pour/music_classifcation"}],"syntology":{"n":419,"n_ran":281,"n_unverified":138,"n_pointer_only":154}}},{"leaderboard":"/sota/image-classification-on-cifar-100","slug":"image-classification-on-cifar-100","dataset":"CIFAR-100","dataset_url":"/dataset/cifar-100","rows_in_archive":211,"metrics":["Percentage correct","PARAMS","Accuracy","Top 1 Accuracy"],"first_row_in_archive_order":{"model":"EffNet-L2 (SAM)","paper_title":"Sharpness-Aware Minimization for Efficiently Improving Generalization","paper_url":"/paper/sharpness-aware-minimization-for-efficiently-1","paper_date":"2020-10-03","arxiv_id":"2010.01412","code_links":[{"title":"davda54/sam","url":"https://github.com/davda54/sam"},{"title":"google-research/sam","url":"https://github.com/google-research/sam"},{"title":"moskomule/sam.pytorch","url":"https://github.com/moskomule/sam.pytorch"},{"title":"simon20010923/DDAMFN","url":"https://github.com/simon20010923/DDAMFN"},{"title":"ys-zong/medfair","url":"https://github.com/ys-zong/medfair"},{"title":"sayakpaul/Sharpness-Aware-Minimization-TensorFlow","url":"https://github.com/sayakpaul/Sharpness-Aware-Minimization-TensorFlow"},{"title":"wangermeng2021/Scaled-YOLOv4-tensorflow2","url":"https://github.com/wangermeng2021/Scaled-YOLOv4-tensorflow2"},{"title":"Jannoshh/simple-sam","url":"https://github.com/Jannoshh/simple-sam"},{"title":"rollovd/LookSAM","url":"https://github.com/rollovd/LookSAM"},{"title":"wangermeng2021/FastClassification","url":"https://github.com/wangermeng2021/FastClassification"},{"title":"borealisai/perturbed-forgetting","url":"https://github.com/borealisai/perturbed-forgetting"},{"title":"mhassann22/GCSAM","url":"https://github.com/mhassann22/GCSAM"},{"title":"Janus-Shiau/SAM-tf2","url":"https://github.com/Janus-Shiau/SAM-tf2"},{"title":"NiMlr/pynlqn","url":"https://github.com/NiMlr/pynlqn"},{"title":"Ashay-20/TF-SAM-Sharpness-Aware-Minimization-Implementation","url":"https://github.com/Ashay-20/TF-SAM-Sharpness-Aware-Minimization-Implementation"},{"title":"Yuheon/Sharp-Aware-Minimization","url":"https://github.com/Yuheon/Sharp-Aware-Minimization"},{"title":"denizyuret/playground","url":"https://github.com/denizyuret/playground"},{"title":"MindCode-4/code-13","url":"https://github.com/MindCode-4/code-13/tree/main/Scalable-Sharpness-Aware-Minimization"}],"syntology":{"n":20,"n_ran":8,"n_unverified":12,"n_pointer_only":6}}},{"leaderboard":"/sota/image-classification-on-stl-10","slug":"image-classification-on-stl-10","dataset":"STL-10","dataset_url":"/dataset/stl-10","rows_in_archive":117,"metrics":["Percentage correct","FLOPS","PARAMS"],"first_row_in_archive_order":{"model":"µ2Net+ (ViT-L/16)","paper_title":"A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems","paper_url":"/paper/a-continual-development-methodology-for-large","paper_date":"2022-09-15","arxiv_id":"2209.07326","code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research/tree/master/muNet"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-objectnet","slug":"image-classification-on-objectnet","dataset":"ObjectNet","dataset_url":"/dataset/objectnet","rows_in_archive":106,"metrics":["Top-1 Accuracy","Top-5 Accuracy"],"first_row_in_archive_order":{"model":"CoCa","paper_title":"CoCa: Contrastive Captioners are Image-Text Foundation Models","paper_url":"/paper/coca-contrastive-captioners-are-image-text","paper_date":"2022-05-04","arxiv_id":"2205.01917","code_links":[{"title":"mlfoundations/open_clip","url":"https://github.com/mlfoundations/open_clip"},{"title":"facebookresearch/multimodal","url":"https://github.com/facebookresearch/multimodal"},{"title":"lucidrains/CoCa-pytorch","url":"https://github.com/lucidrains/CoCa-pytorch"},{"title":"amitakamath/whatsup_vlms","url":"https://github.com/amitakamath/whatsup_vlms"},{"title":"amitakamath/hard_positives","url":"https://github.com/amitakamath/hard_positives"},{"title":"Chaolei98/FreeZAD","url":"https://github.com/Chaolei98/FreeZAD"}],"syntology":{"n":17,"n_ran":9,"n_unverified":8,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-mnist","slug":"image-classification-on-mnist","dataset":"MNIST","dataset_url":"/dataset/mnist","rows_in_archive":81,"metrics":["Percentage error","Accuracy","Trainable Parameters","Cross Entropy Loss","Epochs","Top 1 Accuracy"],"first_row_in_archive_order":{"model":"Branching/Merging CNN + Homogeneous Vector Capsules","paper_title":"No Routing Needed Between Capsules","paper_url":"/paper/a-branching-and-merging-convolutional-network","paper_date":"2020-01-24","arxiv_id":"2001.09136","code_links":[{"title":"AdamByerly/BMCNNwHFCs","url":"https://github.com/AdamByerly/BMCNNwHFCs"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-svhn","slug":"image-classification-on-svhn","dataset":"SVHN","dataset_url":"/dataset/svhn","rows_in_archive":62,"metrics":["Percentage error","Percentage correct"],"first_row_in_archive_order":{"model":"E2E-M3","paper_title":"Rethinking Recurrent Neural Networks and Other Improvements for Image Classification","paper_url":"/paper/rethinking-recurrent-neural-networks-and","paper_date":"2020-07-30","arxiv_id":"2007.15161","code_links":[{"title":"leonlha/e2e-3m","url":"https://github.com/leonlha/e2e-3m"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-inaturalist-2018","slug":"image-classification-on-inaturalist-2018","dataset":"iNaturalist 2018","dataset_url":"/dataset/inaturalist","rows_in_archive":60,"metrics":["Top-1 Accuracy","Number of params"],"first_row_in_archive_order":{"model":"OmniVec2","paper_title":"OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning","paper_url":"/paper/omnivec2-a-novel-transformer-based-network","paper_date":"2024-01-01","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-imagenet-real","slug":"image-classification-on-imagenet-real","dataset":"ImageNet ReaL","dataset_url":null,"rows_in_archive":57,"metrics":["Accuracy","Params","Top 1 Accuracy","Number of params"],"first_row_in_archive_order":{"model":"Baseline (ViT-G/14)","paper_title":"Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","paper_url":"/paper/model-soups-averaging-weights-of-multiple","paper_date":"2022-03-10","arxiv_id":"2203.05482","code_links":[{"title":"mlfoundations/model-soups","url":"https://github.com/mlfoundations/model-soups"},{"title":"Burf/ModelSoups","url":"https://github.com/Burf/ModelSoups"},{"title":"facebookresearch/ModelRatatouille","url":"https://github.com/facebookresearch/ModelRatatouille"},{"title":"hwk0702/keras2torch","url":"https://github.com/hwk0702/keras2torch/tree/main/Computer_Vision/Model_Soup"},{"title":"flowritecom/flow-merge","url":"https://github.com/flowritecom/flow-merge"},{"title":"shallowlearn/sportsreid","url":"https://github.com/shallowlearn/sportsreid"}],"syntology":{"n":17,"n_ran":5,"n_unverified":12,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-flowers-102","slug":"image-classification-on-flowers-102","dataset":"Flowers-102","dataset_url":"/dataset/oxford-102-flower","rows_in_archive":52,"metrics":["Accuracy","FLOPS","PARAMS","Per-Class Accuracy"],"first_row_in_archive_order":{"model":"CCT-14/7x2","paper_title":"Escaping the Big Data Paradigm with Compact Transformers","paper_url":"/paper/escaping-the-big-data-paradigm-with-compact","paper_date":"2021-04-12","arxiv_id":"2104.05704","code_links":[{"title":"keras-team/keras-io","url":"https://github.com/keras-team/keras-io/blob/master/examples/vision/cct.py"},{"title":"SHI-Labs/Compact-Transformers","url":"https://github.com/SHI-Labs/Compact-Transformers"},{"title":"brohrer/sharpened-cosine-similarity","url":"https://github.com/brohrer/sharpened-cosine-similarity"},{"title":"rishikksh20/compact-convolution-transformer","url":"https://github.com/rishikksh20/compact-convolution-transformer"},{"title":"Shreyas-Bhat/CompactTransformers","url":"https://github.com/Shreyas-Bhat/CompactTransformers"},{"title":"stevenwalton/scs-cct","url":"https://github.com/stevenwalton/scs-cct"},{"title":"ahmedelmahy/myownvit","url":"https://github.com/ahmedelmahy/myownvit"},{"title":"Ryul0rd/compact-convolutional-transformer","url":"https://github.com/Ryul0rd/compact-convolutional-transformer"},{"title":"2024-MindSpore-1/Code7","url":"https://github.com/2024-MindSpore-1/Code7/tree/main/cct"}],"syntology":{"n":6,"n_ran":3,"n_unverified":3,"n_pointer_only":1}}},{"leaderboard":"/sota/image-classification-on-clothing1m","slug":"image-classification-on-clothing1m","dataset":"Clothing1M","dataset_url":"/dataset/clothing1m","rows_in_archive":51,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"LRA-diffusion (CC)","paper_title":"Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels","paper_url":"/paper/label-retrieval-augmented-diffusion-models-1","paper_date":"2023-05-31","arxiv_id":"2305.19518","code_links":[{"title":"puar-playground/lra-diffusion","url":"https://github.com/puar-playground/lra-diffusion"}],"syntology":{"n":9,"n_ran":7,"n_unverified":2,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-mini-webvision-1-0","slug":"image-classification-on-mini-webvision-1-0","dataset":"mini WebVision 1.0","dataset_url":"/dataset/webvision-database","rows_in_archive":47,"metrics":["Top-1 Accuracy","Top-5 Accuracy","ImageNet Top-1 Accuracy","ImageNet Top-5 Accuracy"],"first_row_in_archive_order":{"model":"LRA-diffusion (CLIP ViT)","paper_title":"Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels","paper_url":"/paper/label-retrieval-augmented-diffusion-models-1","paper_date":"2023-05-31","arxiv_id":"2305.19518","code_links":[{"title":"puar-playground/lra-diffusion","url":"https://github.com/puar-playground/lra-diffusion"}],"syntology":{"n":9,"n_ran":7,"n_unverified":2,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-fashion-mnist","slug":"image-classification-on-fashion-mnist","dataset":"Fashion-MNIST","dataset_url":"/dataset/fashion-mnist","rows_in_archive":34,"metrics":["Percentage error","Accuracy","Trainable Parameters","NMI","Power consumption"],"first_row_in_archive_order":{"model":"PreAct-ResNet18 + FMix","paper_title":"FMix: Enhancing Mixed Sample Data Augmentation","paper_url":"/paper/understanding-and-enhancing-mixed-sample-data","paper_date":"2020-02-27","arxiv_id":"2002.12047","code_links":[{"title":"PaddlePaddle/PaddleClas","url":"https://github.com/PaddlePaddle/PaddleClas"},{"title":"Westlake-AI/openmixup","url":"https://github.com/Westlake-AI/openmixup"},{"title":"ecs-vlc/FMix","url":"https://github.com/ecs-vlc/FMix"},{"title":"VirajBagal/FMix-Paper-Implementation","url":"https://github.com/VirajBagal/FMix-Paper-Implementation"},{"title":"HyeonhoonLee/MAIC2021_Sleep","url":"https://github.com/HyeonhoonLee/MAIC2021_Sleep"}],"syntology":{"n":8,"n_ran":8,"n_unverified":0,"n_pointer_only":3}}},{"leaderboard":"/sota/image-classification-on-vtab-1k-1","slug":"image-classification-on-vtab-1k-1","dataset":"VTAB-1k","dataset_url":"/dataset/vtab","rows_in_archive":34,"metrics":["Top-1 Accuracy"],"first_row_in_archive_order":{"model":"ALIGN (50 hypers/task)","paper_title":"Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision","paper_url":"/paper/scaling-up-visual-and-vision-language","paper_date":"2021-02-11","arxiv_id":"2102.05918","code_links":[{"title":"facebookresearch/metaclip","url":"https://github.com/facebookresearch/metaclip"},{"title":"kakaobrain/coyo-dataset","url":"https://github.com/kakaobrain/coyo-dataset"},{"title":"MicPie/clasp","url":"https://github.com/MicPie/clasp"},{"title":"willard-yuan/video-text-retrieval-papers","url":"https://github.com/willard-yuan/video-text-retrieval-papers"},{"title":"pwc-1/Paper-8","url":"https://github.com/pwc-1/Paper-8/tree/main/align"}],"syntology":{"n":10,"n_ran":8,"n_unverified":2,"n_pointer_only":9}}},{"leaderboard":"/sota/image-classification-on-imagenet-v2","slug":"image-classification-on-imagenet-v2","dataset":"ImageNet V2","dataset_url":"/dataset/imagenet","rows_in_archive":33,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":"Model soups (BASIC-L)","paper_title":"Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","paper_url":"/paper/model-soups-averaging-weights-of-multiple","paper_date":"2022-03-10","arxiv_id":"2203.05482","code_links":[{"title":"mlfoundations/model-soups","url":"https://github.com/mlfoundations/model-soups"},{"title":"Burf/ModelSoups","url":"https://github.com/Burf/ModelSoups"},{"title":"facebookresearch/ModelRatatouille","url":"https://github.com/facebookresearch/ModelRatatouille"},{"title":"hwk0702/keras2torch","url":"https://github.com/hwk0702/keras2torch/tree/main/Computer_Vision/Model_Soup"},{"title":"flowritecom/flow-merge","url":"https://github.com/flowritecom/flow-merge"},{"title":"shallowlearn/sportsreid","url":"https://github.com/shallowlearn/sportsreid"}],"syntology":{"n":17,"n_ran":5,"n_unverified":12,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-kuzushiji-mnist","slug":"image-classification-on-kuzushiji-mnist","dataset":"Kuzushiji-MNIST","dataset_url":"/dataset/kuzushiji-mnist","rows_in_archive":26,"metrics":["Accuracy","Error","Trainable Parameters"],"first_row_in_archive_order":{"model":"KMNIST-Tiny","paper_title":"Efficient Global Neural Architecture Search","paper_url":"/paper/efficient-global-neural-architecture-search","paper_date":"2025-02-08","arxiv_id":"2502.03553","code_links":[{"title":"siddikui/Efficient-Macro-Micro-NAS","url":"https://github.com/siddikui/Efficient-Macro-Micro-NAS"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-stanford-cars","slug":"image-classification-on-stanford-cars","dataset":"Stanford Cars","dataset_url":"/dataset/stanford-cars","rows_in_archive":24,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"efficient adaptive ensembling","paper_title":"Efficient Adaptive Ensembling for Image Classification","paper_url":"/paper/efficient-adaptive-ensembling-for-image","paper_date":"2022-06-15","arxiv_id":"2206.07394","code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-tiny-imagenet-1","slug":"image-classification-on-tiny-imagenet-1","dataset":"Tiny ImageNet Classification","dataset_url":"/dataset/tiny-imagenet","rows_in_archive":23,"metrics":["Validation Acc"],"first_row_in_archive_order":{"model":"Astroformer","paper_title":"Astroformer: More Data Might not be all you need for Classification","paper_url":"/paper/astroformer-more-data-might-not-be-all-you","paper_date":"2023-04-03","arxiv_id":"2304.05350","code_links":[{"title":"Rishit-dagli/Astroformer","url":"https://github.com/Rishit-dagli/Astroformer"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-inaturalist-2019","slug":"image-classification-on-inaturalist-2019","dataset":"iNaturalist 2019","dataset_url":"/dataset/inaturalist","rows_in_archive":22,"metrics":["Top-1 Accuracy","Number of params"],"first_row_in_archive_order":{"model":"Hiera-H (448px)","paper_title":"Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles","paper_url":"/paper/hiera-a-hierarchical-vision-transformer","paper_date":"2023-06-01","arxiv_id":"2306.00989","code_links":[{"title":"huggingface/pytorch-image-models","url":"https://github.com/huggingface/pytorch-image-models"},{"title":"facebookresearch/hiera","url":"https://github.com/facebookresearch/hiera"},{"title":"leondgarse/keras_cv_attention_models","url":"https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/hiera"},{"title":"birder/birder","url":"https://gitlab.com/birder/birder"}],"syntology":{"n":6,"n_ran":0,"n_unverified":6,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-omnibenchmark","slug":"image-classification-on-omnibenchmark","dataset":"OmniBenchmark","dataset_url":"/dataset/omnibenchmark","rows_in_archive":22,"metrics":["Average Top-1 Accuracy"],"first_row_in_archive_order":{"model":"NOAH-ViTB/16","paper_title":"Neural Prompt Search","paper_url":"/paper/neural-prompt-search","paper_date":"2022-06-09","arxiv_id":"2206.04673","code_links":[{"title":"ZhangYuanhan-AI/NOAH","url":"https://github.com/ZhangYuanhan-AI/NOAH"}],"syntology":{"n":9,"n_ran":6,"n_unverified":3,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-emnist-balanced","slug":"image-classification-on-emnist-balanced","dataset":"EMNIST-Balanced","dataset_url":"/dataset/emnist","rows_in_archive":20,"metrics":["Accuracy","Trainable Parameters","NMI"],"first_row_in_archive_order":{"model":"EMNIST-mobile","paper_title":"Efficient Global Neural Architecture Search","paper_url":"/paper/efficient-global-neural-architecture-search","paper_date":"2025-02-08","arxiv_id":"2502.03553","code_links":[{"title":"siddikui/Efficient-Macro-Micro-NAS","url":"https://github.com/siddikui/Efficient-Macro-Micro-NAS"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-resisc45","slug":"image-classification-on-resisc45","dataset":"RESISC45","dataset_url":"/dataset/resisc45","rows_in_archive":20,"metrics":["Top 1 Accuracy","F1","zero-shot Acc"],"first_row_in_archive_order":{"model":"ResNet50","paper_title":"In-domain representation learning for remote sensing","paper_url":"/paper/in-domain-representation-learning-for-remote-1","paper_date":"2019-11-15","arxiv_id":"1911.06721","code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research/tree/master/remote_sensing_representations"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-df20","slug":"image-classification-on-df20","dataset":"DF20","dataset_url":"/dataset/df20","rows_in_archive":19,"metrics":["Top-1","Top-3","F1 - macro"],"first_row_in_archive_order":{"model":"ViT-Large/16 (384)","paper_title":"Danish Fungi 2020 -- Not Just Another Image Recognition Dataset","paper_url":"/paper/danish-fungi-2020-not-just-another-image","paper_date":"2021-03-18","arxiv_id":"2103.10107","code_links":[{"title":"picekl/DanishFungiDataset","url":"https://github.com/picekl/DanishFungiDataset"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-df20-mini","slug":"image-classification-on-df20-mini","dataset":"DF20 - Mini","dataset_url":"/dataset/df20-mini","rows_in_archive":19,"metrics":["Top-1","Top-3","F1 - macro"],"first_row_in_archive_order":{"model":"ViT-Large/16 (384)","paper_title":"Danish Fungi 2020 -- Not Just Another Image Recognition Dataset","paper_url":"/paper/danish-fungi-2020-not-just-another-image","paper_date":"2021-03-18","arxiv_id":"2103.10107","code_links":[{"title":"picekl/DanishFungiDataset","url":"https://github.com/picekl/DanishFungiDataset"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-inaturalist","slug":"image-classification-on-inaturalist","dataset":"iNaturalist","dataset_url":"/dataset/inaturalist","rows_in_archive":19,"metrics":["Top 1 Accuracy","Top 5 Accuracy","Top 3 Error","Overall"],"first_row_in_archive_order":{"model":"AIMv2-3B (448 res)","paper_title":"Multimodal Autoregressive Pre-training of Large Vision Encoders","paper_url":"/paper/multimodal-autoregressive-pre-training-of","paper_date":"2024-11-21","arxiv_id":"2411.14402","code_links":[{"title":"apple/ml-aim","url":"https://github.com/apple/ml-aim"}],"syntology":{"n":3,"n_ran":0,"n_unverified":3,"n_pointer_only":3}}},{"leaderboard":"/sota/image-classification-on-coloninst-v1-seen","slug":"image-classification-on-coloninst-v1-seen","dataset":"ColonINST-v1 (Seen)","dataset_url":"/dataset/coloninst-v1-seen","rows_in_archive":17,"metrics":["Accuray"],"first_row_in_archive_order":{"model":"ColonGPT (w/ LoRA, w/o extra data)","paper_title":"Frontiers in Intelligent Colonoscopy","paper_url":"/paper/frontiers-in-intelligent-colonoscopy","paper_date":"2024-10-22","arxiv_id":"2410.17241","code_links":[{"title":"ai4colonoscopy/intelliscope","url":"https://github.com/ai4colonoscopy/intelliscope"}],"syntology":{"n":6,"n_ran":0,"n_unverified":6,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-coloninst-v1-unseen","slug":"image-classification-on-coloninst-v1-unseen","dataset":"ColonINST-v1 (Unseen)","dataset_url":"/dataset/coloninst-v1-uneen","rows_in_archive":17,"metrics":["Accuray"],"first_row_in_archive_order":{"model":"ColonGPT (w/ LoRA, w/o extra data)","paper_title":"Frontiers in Intelligent Colonoscopy","paper_url":"/paper/frontiers-in-intelligent-colonoscopy","paper_date":"2024-10-22","arxiv_id":"2410.17241","code_links":[{"title":"ai4colonoscopy/intelliscope","url":"https://github.com/ai4colonoscopy/intelliscope"}],"syntology":{"n":6,"n_ran":0,"n_unverified":6,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-webvision-1000","slug":"image-classification-on-webvision-1000","dataset":"WebVision-1000","dataset_url":"/dataset/webvision-database","rows_in_archive":16,"metrics":["Top-1 Accuracy","Top-5 Accuracy","ImageNet Top-1 Accuracy","ImageNet Top-5 Accuracy"],"first_row_in_archive_order":{"model":"MAM (ViT-B/16)","paper_title":"Improving Image Recognition by Retrieving from Web-Scale Image-Text Data","paper_url":"/paper/improving-image-recognition-by-retrieving","paper_date":"2023-04-11","arxiv_id":"2304.05173","code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-eurosat","slug":"image-classification-on-eurosat","dataset":"EuroSAT","dataset_url":"/dataset/eurosat","rows_in_archive":15,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"DeepEnsembling","paper_title":"Deep Ensembling of Multiband Images for Earth Remote Sensing and Foramnifera Data","paper_url":"/paper/deep-ensembling-of-multiband-images-for-earth","paper_date":"2025-04-02","arxiv_id":null,"code_links":[{"title":"LorisNanni/Multi-Band-Image-Analysis-Using-Ensemble-Neural-Networks","url":"https://github.com/LorisNanni/Multi-Band-Image-Analysis-Using-Ensemble-Neural-Networks"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-places205","slug":"image-classification-on-places205","dataset":"Places205","dataset_url":"/dataset/places205","rows_in_archive":15,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":"InternImage-H","paper_title":"InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions","paper_url":"/paper/internimage-exploring-large-scale-vision","paper_date":"2022-11-10","arxiv_id":"2211.05778","code_links":[{"title":"opengvlab/internimage","url":"https://github.com/opengvlab/internimage"},{"title":"OpenGVLab/M3I-Pretraining","url":"https://github.com/OpenGVLab/M3I-Pretraining"},{"title":"chenller/mmseg-extension","url":"https://github.com/chenller/mmseg-extension"}],"syntology":{"n":4,"n_ran":2,"n_unverified":2,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-dtd","slug":"image-classification-on-dtd","dataset":"DTD","dataset_url":"/dataset/dtd","rows_in_archive":11,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Linear FT(ViT-L/14)","paper_title":"Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models","paper_url":"/paper/task-arithmetic-in-the-tangent-space-improved","paper_date":"2023-05-22","arxiv_id":"2305.12827","code_links":[{"title":"gortizji/tangent_task_arithmetic","url":"https://github.com/gortizji/tangent_task_arithmetic"}],"syntology":{"n":4,"n_ran":1,"n_unverified":3,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-emnist-letters","slug":"image-classification-on-emnist-letters","dataset":"EMNIST-Letters","dataset_url":"/dataset/emnist","rows_in_archive":11,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"WaveMixLite-112/16","paper_title":"WaveMix: A Resource-efficient Neural Network for Image Analysis","paper_url":"/paper/wavemix-lite-a-resource-efficient-neural","paper_date":"2022-05-28","arxiv_id":"2205.14375","code_links":[{"title":"pranavphoenix/WaveMix","url":"https://github.com/pranavphoenix/WaveMix"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-food-101-1","slug":"image-classification-on-food-101-1","dataset":"Food-101","dataset_url":"/dataset/food-101","rows_in_archive":11,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"Bamboo (ViTB/16)","paper_title":"Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy","paper_url":"/paper/bamboo-building-mega-scale-vision-dataset","paper_date":"2022-03-15","arxiv_id":"2203.07845","code_links":[{"title":"zhangyuanhan-ai/bamboo","url":"https://github.com/zhangyuanhan-ai/bamboo"},{"title":"davidzhangyuanhan/bamboo","url":"https://github.com/davidzhangyuanhan/bamboo"}],"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":3}}},{"leaderboard":"/sota/image-classification-on-tcmp-300","slug":"image-classification-on-tcmp-300","dataset":"TCMP-300","dataset_url":"/dataset/tcmp-300","rows_in_archive":11,"metrics":["Accuracy (% )"],"first_row_in_archive_order":{"model":"Swin-Base","paper_title":null,"paper_url":null,"paper_date":"","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-cinic-10","slug":"image-classification-on-cinic-10","dataset":"CINIC-10","dataset_url":"/dataset/cinic-10","rows_in_archive":9,"metrics":["Accuracy","FLOPS","PARAMS"],"first_row_in_archive_order":{"model":"VIT-L/16 (Spinal FC, Background)","paper_title":"Reduction of Class Activation Uncertainty with Background Information","paper_url":"/paper/reduction-of-class-activation-uncertainty","paper_date":"2023-05-05","arxiv_id":"2305.03238","code_links":[{"title":"dipuk0506/SpinalNet","url":"https://github.com/dipuk0506/SpinalNet"},{"title":"dipuk0506/uq","url":"https://github.com/dipuk0506/uq"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-clothing1m-using","slug":"image-classification-on-clothing1m-using","dataset":"Clothing1M (using clean data)","dataset_url":"/dataset/clothing1m","rows_in_archive":9,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"CurriculumNet","paper_title":"CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images","paper_url":"/paper/curriculumnet-weakly-supervised-learning-from","paper_date":"2018-08-03","arxiv_id":"1808.01097","code_links":[{"title":"MalongTech/CurriculumNet","url":"https://github.com/MalongTech/CurriculumNet"},{"title":"guoshengcv/CurriculumNet","url":"https://github.com/guoshengcv/CurriculumNet"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-gashissdb","slug":"image-classification-on-gashissdb","dataset":"GasHisSDB","dataset_url":"/dataset/gashissdb","rows_in_archive":8,"metrics":["Accuracy","Precision","F1-Score"],"first_row_in_archive_order":{"model":"CoAtNet-1","paper_title":"CoAtNet: Marrying Convolution and Attention for All Data Sizes","paper_url":"/paper/coatnet-marrying-convolution-and-attention","paper_date":"2021-06-09","arxiv_id":"2106.04803","code_links":[{"title":"rwightman/pytorch-image-models","url":"https://github.com/rwightman/pytorch-image-models"},{"title":"xmu-xiaoma666/External-Attention-pytorch","url":"https://github.com/xmu-xiaoma666/External-Attention-pytorch/blob/master/model/attention/CoAtNet.py"},{"title":"leondgarse/keras_cv_attention_models","url":"https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/coatnet"},{"title":"chinhsuanwu/coatnet-pytorch","url":"https://github.com/chinhsuanwu/coatnet-pytorch"},{"title":"canturan10/satellighte","url":"https://github.com/canturan10/satellighte"},{"title":"tyeso/Image_Classification_with_CoAtNet_and_ResNet18","url":"https://github.com/tyeso/Image_Classification_with_CoAtNet_and_ResNet18"},{"title":"mindspore-courses/External-Attention-MindSpore","url":"https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/CoAtNet.py"},{"title":"LongLeCE/CoAtNet-PyTorch","url":"https://github.com/LongLeCE/CoAtNet-PyTorch"},{"title":"Burf/CoAtNet-Tensorflow2","url":"https://github.com/Burf/CoAtNet-Tensorflow2"},{"title":"MS-Mind/MS-Code-02","url":"https://github.com/MS-Mind/MS-Code-02/tree/main/configs/coat"},{"title":"pranavsinghps1/dedl","url":"https://github.com/pranavsinghps1/dedl"},{"title":"nqt228/CoAtNet-tensorflow","url":"https://github.com/nqt228/CoAtNet-tensorflow"},{"title":"hw666666666666/CoAtNet","url":"https://github.com/hw666666666666/CoAtNet"},{"title":"Mind23-2/MindCode-19","url":"https://github.com/Mind23-2/MindCode-19"}],"syntology":{"n":5,"n_ran":2,"n_unverified":3,"n_pointer_only":1}}},{"leaderboard":"/sota/image-classification-on-emnist-digits","slug":"image-classification-on-emnist-digits","dataset":"EMNIST-Digits","dataset_url":"/dataset/emnist","rows_in_archive":7,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"WaveMixLite-112/16","paper_title":"WaveMix: A Resource-efficient Neural Network for Image Analysis","paper_url":"/paper/wavemix-lite-a-resource-efficient-neural","paper_date":"2022-05-28","arxiv_id":"2205.14375","code_links":[{"title":"pranavphoenix/WaveMix","url":"https://github.com/pranavphoenix/WaveMix"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-places365","slug":"image-classification-on-places365","dataset":"Places365","dataset_url":"/dataset/places365","rows_in_archive":7,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":"OmniVec2","paper_title":"OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning","paper_url":"/paper/omnivec2-a-novel-transformer-based-network","paper_date":"2024-01-01","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-smallnorb","slug":"image-classification-on-smallnorb","dataset":"smallNORB","dataset_url":"/dataset/smallnorb","rows_in_archive":7,"metrics":["Classification Error"],"first_row_in_archive_order":{"model":"Heinsen Routing","paper_title":"An Algorithm for Routing Capsules in All Domains","paper_url":"/paper/an-algorithm-for-routing-capsules-in-all","paper_date":"2019-11-02","arxiv_id":"1911.00792","code_links":[{"title":"glassroom/heinsen_routing","url":"https://github.com/glassroom/heinsen_routing"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-tiered-imagenet-5-way","slug":"image-classification-on-tiered-imagenet-5-way","dataset":"Tiered ImageNet 5-way (5-shot)","dataset_url":"/dataset/tieredimagenet","rows_in_archive":7,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"EGNN+Transduction","paper_title":"Edge-labeling Graph Neural Network for Few-shot Learning","paper_url":"/paper/edge-labeling-graph-neural-network-for-few","paper_date":"2019-05-04","arxiv_id":"1905.01436","code_links":[{"title":"khy0809/fewshot-egnn","url":"https://github.com/khy0809/fewshot-egnn"},{"title":"dmcv-ecnu/MindSpore_ModelZoo","url":"https://github.com/dmcv-ecnu/MindSpore_ModelZoo/tree/main/EGNN%20Mindspore"},{"title":"xxxnhb/fewshot-egnn","url":"https://github.com/xxxnhb/fewshot-egnn"},{"title":"yjt2018/fewshot-egnn","url":"https://github.com/yjt2018/fewshot-egnn"}],"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-colored-mnist-with","slug":"image-classification-on-colored-mnist-with","dataset":"Colored-MNIST(with spurious correlation)","dataset_url":"/dataset/colored-mnist-spurious-correlation","rows_in_archive":6,"metrics":["Accuracy "],"first_row_in_archive_order":{"model":"MLP-DecAug","paper_title":"DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation","paper_url":"/paper/decaug-out-of-distribution-generalization-via","paper_date":"2020-12-17","arxiv_id":"2012.09382","code_links":[{"title":"HaoyueBaiZJU/DecAug","url":"https://github.com/HaoyueBaiZJU/DecAug"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-iwildcam2020-wilds","slug":"image-classification-on-iwildcam2020-wilds","dataset":"iWildCam2020-WILDS","dataset_url":"/dataset/iwildcam2020-wilds","rows_in_archive":6,"metrics":["Accuracy (Top-1)"],"first_row_in_archive_order":{"model":"COSMO","paper_title":"Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs","paper_url":"/paper/bringing-back-the-context-camera-trap-species","paper_date":"2023-12-31","arxiv_id":"2401.00608","code_links":[{"title":"osu-nlp-group/cosmo","url":"https://github.com/osu-nlp-group/cosmo"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-oxford-iiit-pets-1","slug":"image-classification-on-oxford-iiit-pets-1","dataset":"Oxford-IIIT Pets","dataset_url":"/dataset/oxford-iiit-pets-1","rows_in_archive":6,"metrics":["Accuracy","Per-Class Accuracy"],"first_row_in_archive_order":{"model":"CeiT-S (384 finetune resolution)","paper_title":"Incorporating Convolution Designs into Visual Transformers","paper_url":"/paper/incorporating-convolution-designs-into-visual","paper_date":"2021-03-22","arxiv_id":"2103.11816","code_links":[{"title":"rishikksh20/CeiT-pytorch","url":"https://github.com/rishikksh20/CeiT-pytorch"},{"title":"coeusguo/ceit","url":"https://github.com/coeusguo/ceit"},{"title":"mindspore-courses/External-Attention-MindSpore","url":"https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/CeiT.py"}],"syntology":{"n":11,"n_ran":9,"n_unverified":2,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-caltech-256","slug":"image-classification-on-caltech-256","dataset":"Caltech-256","dataset_url":"/dataset/caltech-256","rows_in_archive":5,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"AG-Net","paper_title":"Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition","paper_url":"/paper/attend-and-guide-ag-net-a-keypoints-driven","paper_date":"2021-10-23","arxiv_id":"2110.12183","code_links":[{"title":"DanielKovach/AG-Net","url":"https://github.com/DanielKovach/AG-Net"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-oxford-iiit-pets","slug":"image-classification-on-oxford-iiit-pets","dataset":"Oxford-IIIT Pet Dataset","dataset_url":"/dataset/oxford-iiit-pets","rows_in_archive":5,"metrics":["Accuracy","PARAMS","FLOPS"],"first_row_in_archive_order":{"model":"TWIST (ResNet-50)","paper_title":"Self-Supervised Learning by Estimating Twin Class Distributions","paper_url":"/paper/self-supervised-learning-by-estimating-twin-1","paper_date":"2021-10-14","arxiv_id":"2110.07402","code_links":[{"title":"bytedance/TWIST","url":"https://github.com/bytedance/TWIST"},{"title":"beresandras/contrastive-classification-keras","url":"https://github.com/beresandras/contrastive-classification-keras"}],"syntology":{"n":15,"n_ran":5,"n_unverified":10,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-red-miniimagenet-20","slug":"image-classification-on-red-miniimagenet-20","dataset":"Red MiniImageNet 20% label noise","dataset_url":"/dataset/red-miniimagenet-20-label-noise","rows_in_archive":5,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"NCR (ResNet-18)","paper_title":"Learning with Neighbor Consistency for Noisy Labels","paper_url":"/paper/learning-with-neighbor-consistency-for-noisy-1","paper_date":"2022-02-04","arxiv_id":"2202.02200","code_links":[{"title":"google-research/scenic","url":"https://github.com/google-research/scenic"}],"syntology":{"n":4,"n_ran":4,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-red-miniimagenet-40","slug":"image-classification-on-red-miniimagenet-40","dataset":"Red MiniImageNet 40% label noise","dataset_url":"/dataset/red-miniimagenet-40-label-noise","rows_in_archive":5,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"NCR (ResNet-18)","paper_title":"Learning with Neighbor Consistency for Noisy Labels","paper_url":"/paper/learning-with-neighbor-consistency-for-noisy-1","paper_date":"2022-02-04","arxiv_id":"2202.02200","code_links":[{"title":"google-research/scenic","url":"https://github.com/google-research/scenic"}],"syntology":{"n":4,"n_ran":4,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-red-miniimagenet-80","slug":"image-classification-on-red-miniimagenet-80","dataset":"Red MiniImageNet 80% label noise","dataset_url":"/dataset/red-miniimagenet-80-label-noise","rows_in_archive":5,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"NCR (ResNet-18)","paper_title":"Learning with Neighbor Consistency for Noisy Labels","paper_url":"/paper/learning-with-neighbor-consistency-for-noisy-1","paper_date":"2022-02-04","arxiv_id":"2202.02200","code_links":[{"title":"google-research/scenic","url":"https://github.com/google-research/scenic"}],"syntology":{"n":4,"n_ran":4,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-cifar-10-with-noisy","slug":"image-classification-on-cifar-10-with-noisy","dataset":"CIFAR-10 (with noisy labels)","dataset_url":"/dataset/cifar-10","rows_in_archive":4,"metrics":["Accuracy (under 20% Sym. label noise)","Accuracy (under 50% Sym. label noise)","Accuracy (under 80% Sym. label noise)","Accuracy (under 90% Sym. label noise)","Accuracy (under 95% Sym. label noise)"],"first_row_in_archive_order":{"model":"SSR","paper_title":"SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise","paper_url":"/paper/s3-supervised-self-supervised-learning-under-1","paper_date":"2021-11-22","arxiv_id":"2111.11288","code_links":[{"title":"MrChenFeng/SSR_BMVC2022","url":"https://github.com/MrChenFeng/SSR_BMVC2022"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-cub","slug":"image-classification-on-cub","dataset":"CUB","dataset_url":"/dataset/cub-200-2011","rows_in_archive":4,"metrics":["Classification Accuracy","Explanation Accuracy","Explanation complexity","Explanation extraction time"],"first_row_in_archive_order":{"model":"Entropy-based Logic Explained Network","paper_title":"Entropy-based Logic Explanations of Neural Networks","paper_url":"/paper/entropy-based-logic-explanations-of-neural","paper_date":"2021-06-12","arxiv_id":"2106.06804","code_links":[{"title":"pietrobarbiero/pytorch_explain","url":"https://github.com/pietrobarbiero/pytorch_explain"},{"title":"pietrobarbiero/logic_explainer_networks","url":"https://github.com/pietrobarbiero/logic_explainer_networks"},{"title":"pietrobarbiero/entropy-lens","url":"https://github.com/pietrobarbiero/entropy-lens"}],"syntology":{"n":6,"n_ran":1,"n_unverified":5,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-food-101n-1","slug":"image-classification-on-food-101n-1","dataset":"Food-101N","dataset_url":"/dataset/food-101n","rows_in_archive":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"LRA-diffusion (CLIP ViT)","paper_title":"Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels","paper_url":"/paper/label-retrieval-augmented-diffusion-models-1","paper_date":"2023-05-31","arxiv_id":"2305.19518","code_links":[{"title":"puar-playground/lra-diffusion","url":"https://github.com/puar-playground/lra-diffusion"}],"syntology":{"n":9,"n_ran":7,"n_unverified":2,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-isic2018","slug":"image-classification-on-isic2018","dataset":"ISIC2018","dataset_url":null,"rows_in_archive":4,"metrics":["Accuracy","F1"],"first_row_in_archive_order":{"model":"UniNet","paper_title":"UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection","paper_url":"/paper/uninet-a-contrastive-learning-guided-unified","paper_date":"2025-02-28","arxiv_id":null,"code_links":[{"title":"pangdatangtt/UniNet","url":"https://github.com/pangdatangtt/UniNet"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-jft-300m","slug":"image-classification-on-jft-300m","dataset":"JFT-300M","dataset_url":"/dataset/jft-300m","rows_in_archive":4,"metrics":["prec@1"],"first_row_in_archive_order":{"model":"V-MoE-H/14 (Every-2)","paper_title":"Scaling Vision with Sparse Mixture of Experts","paper_url":"/paper/scaling-vision-with-sparse-mixture-of-experts","paper_date":"2021-06-10","arxiv_id":"2106.05974","code_links":[{"title":"google-research/vmoe","url":"https://github.com/google-research/vmoe"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-mame","slug":"image-classification-on-mame","dataset":"MAMe","dataset_url":"/dataset/mame","rows_in_archive":4,"metrics":["Acc"],"first_row_in_archive_order":{"model":"EfficientNet-B3","paper_title":"The MAMe Dataset: On the relevance of High Resolution and Variable Shape image properties","paper_url":"/paper/a-closer-look-at-art-mediums-the-mame-image","paper_date":"2020-07-27","arxiv_id":"2007.13693","code_links":[{"title":"HPAI-BSC/MAMe-baselines","url":"https://github.com/HPAI-BSC/MAMe-baselines"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-n-mnist","slug":"image-classification-on-n-mnist","dataset":"N-MNIST","dataset_url":"/dataset/n-mnist","rows_in_archive":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"STS-ResNet","paper_title":"Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction","paper_url":"/paper/convolutional-spiking-neural-networks-for","paper_date":"2020-03-27","arxiv_id":"2003.12346","code_links":[{"title":"aa-samad/conv_snn","url":"https://github.com/aa-samad/conv_snn"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-objectnet-bounding","slug":"image-classification-on-objectnet-bounding","dataset":"ObjectNet (Bounding Box)","dataset_url":"/dataset/objectnet","rows_in_archive":4,"metrics":["Top 5 Accuracy"],"first_row_in_archive_order":{"model":"BiT-L (ResNet)","paper_title":"Big Transfer (BiT): General Visual Representation Learning","paper_url":"/paper/large-scale-learning-of-general-visual","paper_date":"2019-12-24","arxiv_id":"1912.11370","code_links":[{"title":"google-research/big_transfer","url":"https://github.com/google-research/big_transfer"},{"title":"sayakpaul/FunMatch-Distillation","url":"https://github.com/sayakpaul/FunMatch-Distillation"},{"title":"bethgelab/InDomainGeneralizationBenchmark","url":"https://github.com/bethgelab/InDomainGeneralizationBenchmark"},{"title":"SoojungYang/supervised_pretraining_GN_WS","url":"https://github.com/SoojungYang/supervised_pretraining_GN_WS"},{"title":"sayakpaul/A-Barebones-Image-Retrieval-System","url":"https://github.com/sayakpaul/A-Barebones-Image-Retrieval-System"},{"title":"batsresearch/taglets","url":"https://github.com/batsresearch/taglets"},{"title":"MS-Mind/MS-Code-02","url":"https://github.com/MS-Mind/MS-Code-02/tree/main/configs/bit"},{"title":"2024-MindSpore-1/Code2","url":"https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bit"},{"title":"hw666666666666/BigTransfer","url":"https://github.com/hw666666666666/BigTransfer"}],"syntology":{"n":10,"n_ran":3,"n_unverified":7,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-oracle-mnist","slug":"image-classification-on-oracle-mnist","dataset":"Oracle-MNIST","dataset_url":"/dataset/oracle-mnist","rows_in_archive":4,"metrics":["Accuracy","Trainable Parameters"],"first_row_in_archive_order":{"model":"ResNet-18 + Vision Eagle Attention","paper_title":"Vision Eagle Attention: a new lens for advancing image classification","paper_url":"/paper/vision-eagle-attention-a-new-lens-for","paper_date":"2024-11-15","arxiv_id":"2411.10564","code_links":[{"title":"MahmudulHasan11085/Vision-Eagle-Attention","url":"https://github.com/MahmudulHasan11085/Vision-Eagle-Attention"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-places365-standard","slug":"image-classification-on-places365-standard","dataset":"Places365-Standard","dataset_url":"/dataset/places365","rows_in_archive":4,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":"SWAG (ViT H/14)","paper_title":"Revisiting Weakly Supervised Pre-Training of Visual Perception Models","paper_url":"/paper/revisiting-weakly-supervised-pre-training-of","paper_date":"2022-01-20","arxiv_id":"2201.08371","code_links":[{"title":"facebookresearch/SWAG","url":"https://github.com/facebookresearch/SWAG"},{"title":"Expedit-LargeScale-Vision-Transformer/Expedit-SWAG","url":"https://github.com/Expedit-LargeScale-Vision-Transformer/Expedit-SWAG"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-red-miniimagenet-60","slug":"image-classification-on-red-miniimagenet-60","dataset":"Red MiniImageNet 60% label noise","dataset_url":null,"rows_in_archive":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"InstanceGM-SS","paper_title":"Instance-Dependent Noisy Label Learning via Graphical Modelling","paper_url":"/paper/instance-dependent-noisy-label-learning-via","paper_date":"2022-09-02","arxiv_id":"2209.00906","code_links":[{"title":"arpit2412/InstanceGM","url":"https://github.com/arpit2412/InstanceGM"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-tiny-imagenet-2","slug":"image-classification-on-tiny-imagenet-2","dataset":"Tiny-ImageNet","dataset_url":"/dataset/tiny-imagenet","rows_in_archive":4,"metrics":["Top 1 Accuracy","Top-1 Accuracy"],"first_row_in_archive_order":{"model":"UPANets","paper_title":"UPANets: Learning from the Universal Pixel Attention Networks","paper_url":"/paper/upanets-learning-from-the-universal-pixel","paper_date":"2021-03-15","arxiv_id":"2103.08640","code_links":[{"title":"hanktseng131415go/UPANets","url":"https://github.com/hanktseng131415go/UPANets"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-visual-wake-words","slug":"image-classification-on-visual-wake-words","dataset":"Visual Wake Words","dataset_url":"/dataset/visual-wake-words","rows_in_archive":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"HyT-NAS-BA","paper_title":"HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices","paper_url":"/paper/hyt-nas-hybrid-transformers-neural","paper_date":"2023-03-08","arxiv_id":"2303.04440","code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-breakhis","slug":"image-classification-on-breakhis","dataset":"BreakHis","dataset_url":"/dataset/breakhis","rows_in_archive":3,"metrics":["Average Test Accuracy over all magnifications"],"first_row_in_archive_order":{"model":"WaveMix","paper_title":"Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision","paper_url":"/paper/which-backbone-to-use-a-resource-efficient","paper_date":"2024-06-09","arxiv_id":"2406.05612","code_links":[{"title":"pranavphoenix/Backbones","url":"https://github.com/pranavphoenix/Backbones"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-celeba-64x64","slug":"image-classification-on-celeba-64x64","dataset":"CelebA 64x64","dataset_url":"/dataset/celeba","rows_in_archive":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"cFlow","paper_title":"Null-sampling for Interpretable and Fair Representations","paper_url":"/paper/null-sampling-for-interpretable-and-fair","paper_date":"2020-08-12","arxiv_id":"2008.05248","code_links":[{"title":"predictive-analytics-lab/nifr","url":"https://github.com/predictive-analytics-lab/nifr"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-earlynsd","slug":"image-classification-on-earlynsd","dataset":"EarlyNSD","dataset_url":"/dataset/earlynsd","rows_in_archive":3,"metrics":["Test accuracy","Test f1"],"first_row_in_archive_order":{"model":"DenseNet121_256x256_Nutrispace","paper_title":"Nutrispace: A novel color space to enhance deep learning based early detection of cucurbits nutritional deficiency","paper_url":"/paper/nutrispace-a-novel-color-space-to-enhance","paper_date":"2024-07-18","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-eurosat-sar","slug":"image-classification-on-eurosat-sar","dataset":"EuroSAT-SAR","dataset_url":"/dataset/eurosat-sar","rows_in_archive":3,"metrics":["Overall Accuracy"],"first_row_in_archive_order":{"model":"FG-MAE (ViT-S/16)","paper_title":"Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing","paper_url":"/paper/feature-guided-masked-autoencoder-for-self","paper_date":"2023-10-28","arxiv_id":"2310.18653","code_links":[{"title":"zhu-xlab/fgmae","url":"https://github.com/zhu-xlab/fgmae"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-flickrlogos-32","slug":"image-classification-on-flickrlogos-32","dataset":"FlickrLogos-32","dataset_url":"/dataset/flickrlogos-32","rows_in_archive":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"TC-VII (with outside data)","paper_title":"Deep Learning for Logo Recognition","paper_url":"/paper/deep-learning-for-logo-recognition","paper_date":"2017-01-10","arxiv_id":"1701.02620","code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-id-pattern-dataset","slug":"image-classification-on-id-pattern-dataset","dataset":"Id Pattern Dataset","dataset_url":"/dataset/id-pattern-dataset","rows_in_archive":3,"metrics":["Percentage correct"],"first_row_in_archive_order":{"model":"Claude 3 Opus","paper_title":"Identification of Stone Deterioration Patterns with Large Multimodal Models","paper_url":"/paper/identification-of-stone-deterioration","paper_date":"2024-06-05","arxiv_id":"2406.03207","code_links":[{"title":"dcorradetti/redai_id_pattern","url":"https://github.com/dcorradetti/redai_id_pattern"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-kvasir","slug":"image-classification-on-kvasir","dataset":"Kvasir","dataset_url":"/dataset/kvasir","rows_in_archive":3,"metrics":["Accuracy","F1"],"first_row_in_archive_order":{"model":"HiFuse_Small","paper_title":"HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification","paper_url":"/paper/hifuse-hierarchical-multi-scale-feature","paper_date":"2022-09-21","arxiv_id":"2209.10218","code_links":[{"title":"huoxiangzuo/HiFuse","url":"https://github.com/huoxiangzuo/HiFuse"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-malaria-dataset","slug":"image-classification-on-malaria-dataset","dataset":"Malaria Dataset","dataset_url":"/dataset/malaria-dataset","rows_in_archive":3,"metrics":["Acc. (test)","PARAMS"],"first_row_in_archive_order":{"model":"kEffNet-B0 V2 16ch","paper_title":"An Enhanced Scheme for Reducing the Complexity of Pointwise Convolutions in CNNs for Image Classification Based on Interleaved Grouped Filters without Divisibility Constraints","paper_url":"/paper/an-enhanced-scheme-for-reducing-the","paper_date":"2022-09-08","arxiv_id":null,"code_links":[{"title":"joaopauloschuler/k-neural-api","url":"https://github.com/joaopauloschuler/k-neural-api"},{"title":"joaopauloschuler/kEffNetV2","url":"https://github.com/joaopauloschuler/kEffNetV2"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-n-caltech-101","slug":"image-classification-on-n-caltech-101","dataset":"N-Caltech 101","dataset_url":"/dataset/n-caltech-101","rows_in_archive":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"mMND (STDP)","paper_title":"Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods","paper_url":"/paper/sequence-approximation-using-feedforward","paper_date":"2021-09-29","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-sipakmed","slug":"image-classification-on-sipakmed","dataset":"SIPaKMeD","dataset_url":"/dataset/sipakmed","rows_in_archive":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"DL+PCA+GWO","paper_title":"Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection","paper_url":"/paper/cervical-cytology-classification-using-pca","paper_date":"2021-06-09","arxiv_id":"2106.04919","code_links":[{"title":"Rohit-Kundu/Two-Step-Feature-Enhancement","url":"https://github.com/Rohit-Kundu/Two-Step-Feature-Enhancement"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-causal3dident","slug":"image-classification-on-causal3dident","dataset":"Causal3DIdent","dataset_url":"/dataset/causal3dident","rows_in_archive":2,"metrics":["Accuracy "],"first_row_in_archive_order":{"model":"SimCLR","paper_title":"Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style","paper_url":"/paper/self-supervised-learning-with-data","paper_date":"2021-06-08","arxiv_id":"2106.04619","code_links":[{"title":"ysharma1126/ssl_identifiability","url":"https://github.com/ysharma1126/ssl_identifiability"}],"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-certificate","slug":"image-classification-on-certificate","dataset":"Certificate Verification","dataset_url":null,"rows_in_archive":2,"metrics":["Percentage correct","Top-1 Accuracy"],"first_row_in_archive_order":{"model":"ResMLP-24","paper_title":"ResMLP: Feedforward networks for image classification with data-efficient training","paper_url":"/paper/resmlp-feedforward-networks-for-image","paper_date":"2021-05-07","arxiv_id":"2105.03404","code_links":[{"title":"rwightman/pytorch-image-models","url":"https://github.com/rwightman/pytorch-image-models"},{"title":"xmu-xiaoma666/External-Attention-pytorch","url":"https://github.com/xmu-xiaoma666/External-Attention-pytorch"},{"title":"facebookresearch/deit","url":"https://github.com/facebookresearch/deit"},{"title":"BR-IDL/PaddleViT","url":"https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/ResMLP"},{"title":"leondgarse/keras_cv_attention_models","url":"https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/mlp_family"},{"title":"martinsbruveris/tensorflow-image-models","url":"https://github.com/martinsbruveris/tensorflow-image-models"},{"title":"Mayurji/Image-Classification-PyTorch","url":"https://github.com/Mayurji/Image-Classification-PyTorch"},{"title":"lucidrains/res-mlp-pytorch","url":"https://github.com/lucidrains/res-mlp-pytorch"},{"title":"liuruiyang98/Jittor-MLP","url":"https://github.com/liuruiyang98/Jittor-MLP"},{"title":"lalithjets/surgical_vqa","url":"https://github.com/lalithjets/surgical_vqa"},{"title":"rishikksh20/ResMLP-pytorch","url":"https://github.com/rishikksh20/ResMLP-pytorch"},{"title":"megvii-research/basecls","url":"https://github.com/megvii-research/basecls/tree/main/zoo/public/resmlp"},{"title":"leaderj1001/Bag-of-MLP","url":"https://github.com/leaderj1001/Bag-of-MLP"},{"title":"birder/birder","url":"https://gitlab.com/birder/birder"},{"title":"IMvision12/keras-vision-models","url":"https://github.com/IMvision12/keras-vision-models"},{"title":"jaketae/res-mlp","url":"https://github.com/jaketae/res-mlp"},{"title":"MindCode-4/code-8","url":"https://github.com/MindCode-4/code-8/tree/main/res_mlp_ms"},{"title":"MindCode-4/code-13","url":"https://github.com/MindCode-4/code-13/tree/main/res_mlp_ms"},{"title":"yeyinthtoon/tf2-resmlp","url":"https://github.com/yeyinthtoon/tf2-resmlp"}],"syntology":{"n":7,"n_ran":2,"n_unverified":5,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-cifar-10-40-labels","slug":"image-classification-on-cifar-10-40-labels","dataset":"CIFAR-10 (40 Labels, ImageNet-100 Unlabeled)","dataset_url":null,"rows_in_archive":2,"metrics":["Accuarcy"],"first_row_in_archive_order":{"model":"UnMixMatch","paper_title":"Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data","paper_url":"/paper/scaling-up-semi-supervised-learning-with","paper_date":"2023-06-02","arxiv_id":"2306.01222","code_links":[{"title":"shuvenduroy/unmixmatch","url":"https://github.com/shuvenduroy/unmixmatch"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-cifar-10-40-symmetric","slug":"image-classification-on-cifar-10-40-symmetric","dataset":"CIFAR-10, 40% Symmetric Noise","dataset_url":"/dataset/cifar-10","rows_in_archive":2,"metrics":["Percentage correct"],"first_row_in_archive_order":{"model":"FaMUS","paper_title":"Faster Meta Update Strategy for Noise-Robust Deep Learning","paper_url":"/paper/faster-meta-update-strategy-for-noise-robust","paper_date":"2021-04-30","arxiv_id":"2104.15092","code_links":[{"title":"youjiangxu/FaMUS","url":"https://github.com/youjiangxu/FaMUS"}],"syntology":{"n":7,"n_ran":2,"n_unverified":5,"n_pointer_only":7}}},{"leaderboard":"/sota/image-classification-on-cifar-10-60-symmetric","slug":"image-classification-on-cifar-10-60-symmetric","dataset":"CIFAR-10, 60% Symmetric Noise","dataset_url":"/dataset/cifar-10","rows_in_archive":2,"metrics":["Percentage correct"],"first_row_in_archive_order":{"model":"MentorMix","paper_title":"Faster Meta Update Strategy for Noise-Robust Deep Learning","paper_url":"/paper/faster-meta-update-strategy-for-noise-robust","paper_date":"2021-04-30","arxiv_id":"2104.15092","code_links":[{"title":"youjiangxu/FaMUS","url":"https://github.com/youjiangxu/FaMUS"}],"syntology":{"n":7,"n_ran":2,"n_unverified":5,"n_pointer_only":7}}},{"leaderboard":"/sota/image-classification-on-cifar-10-image","slug":"image-classification-on-cifar-10-image","dataset":"CIFAR-10 Image Classification","dataset_url":"/dataset/cifar-10","rows_in_archive":2,"metrics":["Params"],"first_row_in_archive_order":{"model":"ASF-former-S","paper_title":"Adaptive Split-Fusion Transformer","paper_url":"/paper/adaptive-split-fusion-transformer","paper_date":"2022-04-26","arxiv_id":"2204.12196","code_links":[{"title":"szx503045266/asf-former","url":"https://github.com/szx503045266/asf-former"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-cifar-100-40","slug":"image-classification-on-cifar-100-40","dataset":"CIFAR-100, 40% Symmetric Noise","dataset_url":null,"rows_in_archive":2,"metrics":["Percentage correct"],"first_row_in_archive_order":{"model":"FaMUS","paper_title":"Faster Meta Update Strategy for Noise-Robust Deep Learning","paper_url":"/paper/faster-meta-update-strategy-for-noise-robust","paper_date":"2021-04-30","arxiv_id":"2104.15092","code_links":[{"title":"youjiangxu/FaMUS","url":"https://github.com/youjiangxu/FaMUS"}],"syntology":{"n":7,"n_ran":2,"n_unverified":5,"n_pointer_only":7}}},{"leaderboard":"/sota/image-classification-on-clevr-count","slug":"image-classification-on-clevr-count","dataset":"CLEVR/Count","dataset_url":null,"rows_in_archive":2,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":"SEER (RegNet10B)","paper_title":"Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision","paper_url":"/paper/vision-models-are-more-robust-and-fair-when","paper_date":"2022-02-16","arxiv_id":"2202.08360","code_links":[{"title":"facebookresearch/vissl","url":"https://github.com/facebookresearch/vissl"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-clevr-dist","slug":"image-classification-on-clevr-dist","dataset":"CLEVR/Dist","dataset_url":null,"rows_in_archive":2,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":"SEER (RegNet10B)","paper_title":"Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision","paper_url":"/paper/vision-models-are-more-robust-and-fair-when","paper_date":"2022-02-16","arxiv_id":"2202.08360","code_links":[{"title":"facebookresearch/vissl","url":"https://github.com/facebookresearch/vissl"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-cub-200-2011-3","slug":"image-classification-on-cub-200-2011-3","dataset":"CUB-200-2011","dataset_url":"/dataset/cub-200-2011","rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Sparse-CBM","paper_title":"Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning","paper_url":"/paper/sparse-concept-bottleneck-models-gumbel","paper_date":"2024-04-04","arxiv_id":"2404.03323","code_links":[{"title":"andron00e/sparsecbm","url":"https://github.com/andron00e/sparsecbm"},{"title":"icml24/sparsecbm","url":"https://github.com/icml24/sparsecbm"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-fracture-normal","slug":"image-classification-on-fracture-normal","dataset":"Fracture/Normal Shoulder Bone X-ray Images on MURA","dataset_url":null,"rows_in_archive":2,"metrics":["Cohen’s Kappa score ","Test Accuracy","AUC score"],"first_row_in_archive_order":{"model":"Our Ensemble Learning-2","paper_title":"Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models","paper_url":"/paper/classification-of-fracture-and-normal","paper_date":"2021-01-31","arxiv_id":"2102.00515","code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-galaxy10-decals","slug":"image-classification-on-galaxy10-decals","dataset":"Galaxy10 DECals","dataset_url":"/dataset/galaxy-zoo-decals","rows_in_archive":2,"metrics":["Top-1 Accuracy (%)","PARAMS (M)"],"first_row_in_archive_order":{"model":"WaveMix","paper_title":"WaveMix: A Resource-efficient Neural Network for Image Analysis","paper_url":"/paper/wavemix-lite-a-resource-efficient-neural","paper_date":"2022-05-28","arxiv_id":"2205.14375","code_links":[{"title":"pranavphoenix/WaveMix","url":"https://github.com/pranavphoenix/WaveMix"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-gaze-cifar-10","slug":"image-classification-on-gaze-cifar-10","dataset":"Gaze-CIFAR-10","dataset_url":"/dataset/gaze-cifar-10","rows_in_archive":2,"metrics":["1:1 Accuracy"],"first_row_in_archive_order":{"model":"DSGE-ConvNeXtV2","paper_title":"Gaze-Guided Learning: Avoiding Shortcut Bias in Visual Classification","paper_url":"/paper/gaze-guided-learning-avoiding-shortcut-bias","paper_date":"2025-04-08","arxiv_id":"2504.05583","code_links":[{"title":"rekkles2/Gaze-CIFAR-10","url":"https://github.com/rekkles2/Gaze-CIFAR-10"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-herlev","slug":"image-classification-on-herlev","dataset":"HErlev","dataset_url":"/dataset/herlev","rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Fuzzy Distance Ensemble","paper_title":"A fuzzy distance-based ensemble of deep models for cervical cancer detection","paper_url":"/paper/a-fuzzy-distance-based-ensemble-of-deep","paper_date":"2022-03-30","arxiv_id":null,"code_links":[{"title":"rishavpramanik/CervicalFuzzyDistanceEnsemble","url":"https://github.com/rishavpramanik/CervicalFuzzyDistanceEnsemble"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-imagenet-10","slug":"image-classification-on-imagenet-10","dataset":"ImageNet-10","dataset_url":null,"rows_in_archive":2,"metrics":["Top 1 Accuracy","ARI"],"first_row_in_archive_order":{"model":"ResNet-50 + UDA+AutoDropout","paper_title":"AutoDropout: Learning Dropout Patterns to Regularize Deep Networks","paper_url":"/paper/autodropout-learning-dropout-patterns-to","paper_date":"2021-01-05","arxiv_id":"2101.01761","code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-imagenet-100","slug":"image-classification-on-imagenet-100","dataset":"ImageNet-100 (TEMI Split)","dataset_url":"/dataset/imagenet-100","rows_in_archive":2,"metrics":["Percentage correct","Params"],"first_row_in_archive_order":{"model":"SparseSwin with L2","paper_title":"SparseSwin: Swin Transformer with Sparse Transformer Block","paper_url":"/paper/sparseswin-swin-transformer-with-sparse","paper_date":"2023-09-11","arxiv_id":"2309.05224","code_links":[{"title":"krisnapinasthika/sparseswin","url":"https://github.com/krisnapinasthika/sparseswin"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-imagenet-hard","slug":"image-classification-on-imagenet-hard","dataset":"ImageNet-Hard","dataset_url":"/dataset/imagenet-hard","rows_in_archive":2,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"EfficientNet-L2-Ns","paper_title":null,"paper_url":null,"paper_date":"","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-imagenette","slug":"image-classification-on-imagenette","dataset":"Imagenette","dataset_url":"/dataset/imagenette","rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"µ2Net+ (ViT-L/16)","paper_title":"A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems","paper_url":"/paper/a-continual-development-methodology-for-large","paper_date":"2022-09-15","arxiv_id":"2209.07326","code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research/tree/master/muNet"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-imbalanced-cub-200","slug":"image-classification-on-imbalanced-cub-200","dataset":"Imbalanced CUB-200-2011","dataset_url":"/dataset/cub-200-2011","rows_in_archive":2,"metrics":["Accuracy","Average Per-Class Accuracy"],"first_row_in_archive_order":{"model":"Multi-task","paper_title":"A New Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios","paper_url":"/paper/ufpr-periocular-a-periocular-dataset","paper_date":"2020-11-24","arxiv_id":"2011.12427","code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-intel-image","slug":"image-classification-on-intel-image","dataset":"Intel Image Classification","dataset_url":"/dataset/intel-image-classification","rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"ResNet-18 + Vision Eagle Attention","paper_title":"Vision Eagle Attention: a new lens for advancing image classification","paper_url":"/paper/vision-eagle-attention-a-new-lens-for","paper_date":"2024-11-15","arxiv_id":"2411.10564","code_links":[{"title":"MahmudulHasan11085/Vision-Eagle-Attention","url":"https://github.com/MahmudulHasan11085/Vision-Eagle-Attention"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-isic-2018","slug":"image-classification-on-isic-2018","dataset":"ISIC 2018","dataset_url":null,"rows_in_archive":2,"metrics":["F1"],"first_row_in_archive_order":{"model":"HiFuse_Base","paper_title":"HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification","paper_url":"/paper/hifuse-hierarchical-multi-scale-feature","paper_date":"2022-09-21","arxiv_id":"2209.10218","code_links":[{"title":"huoxiangzuo/HiFuse","url":"https://github.com/huoxiangzuo/HiFuse"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-large-labelled-logo","slug":"image-classification-on-large-labelled-logo","dataset":"Large Labelled Logo Dataset (L3D)","dataset_url":"/dataset/large-labelled-logo-dataset-l3d","rows_in_archive":2,"metrics":["Eval F1"],"first_row_in_archive_order":{"model":"L3D_original_2level","paper_title":"The Large Labelled Logo Dataset (L3D): A Multipurpose and Hand-Labelled Continuously Growing Dataset","paper_url":"/paper/the-large-labelled-logo-dataset-l3d-a","paper_date":"2021-12-10","arxiv_id":"2112.05404","code_links":[{"title":"lhf-labs/tm-dataset","url":"https://github.com/lhf-labs/tm-dataset"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-limuc","slug":"image-classification-on-limuc","dataset":"LIMUC","dataset_url":"/dataset/limuc","rows_in_archive":2,"metrics":["Quadratic Weighted Kappa"],"first_row_in_archive_order":{"model":"Inception-v3","paper_title":"Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation","paper_url":"/paper/class-distance-weighted-cross-entropy-loss","paper_date":"2022-02-09","arxiv_id":"2202.05167","code_links":[{"title":"GorkemP/labeled-images-for-ulcerative-colitis","url":"https://github.com/GorkemP/labeled-images-for-ulcerative-colitis"}],"syntology":{"n":7,"n_ran":0,"n_unverified":7,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-noisy-mnist-awgn","slug":"image-classification-on-noisy-mnist-awgn","dataset":"Noisy MNIST (AWGN)","dataset_url":"/dataset/mnist","rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"PCGAN-CHAR","paper_title":"PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters","paper_url":"/paper/pcgan-char-progressively-trained-classifier","paper_date":"2019-08-11","arxiv_id":"1908.08987","code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-noisy-mnist-contrast","slug":"image-classification-on-noisy-mnist-contrast","dataset":"Noisy MNIST (Contrast)","dataset_url":"/dataset/mnist","rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"PCGAN-CHAR","paper_title":"PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters","paper_url":"/paper/pcgan-char-progressively-trained-classifier","paper_date":"2019-08-11","arxiv_id":"1908.08987","code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-noisy-mnist-motion","slug":"image-classification-on-noisy-mnist-motion","dataset":"Noisy MNIST (Motion)","dataset_url":"/dataset/mnist","rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"PCGAN-CHAR","paper_title":"PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters","paper_url":"/paper/pcgan-char-progressively-trained-classifier","paper_date":"2019-08-11","arxiv_id":"1908.08987","code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-objectnet-imagenet","slug":"image-classification-on-objectnet-imagenet","dataset":"ObjectNet (ImageNet classes)","dataset_url":null,"rows_in_archive":2,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":"Diffusion Classifier (zero-shot)","paper_title":"Your Diffusion Model is Secretly a Zero-Shot Classifier","paper_url":"/paper/your-diffusion-model-is-secretly-a-zero-shot","paper_date":"2023-03-28","arxiv_id":"2303.16203","code_links":[{"title":"diffusion-classifier/diffusion-classifier","url":"https://github.com/diffusion-classifier/diffusion-classifier"},{"title":"SamsungSAILMontreal/ForestDiffusion","url":"https://github.com/SamsungSAILMontreal/ForestDiffusion"},{"title":"tajamul21/fate","url":"https://github.com/tajamul21/fate"},{"title":"LiYinqi/DIVE","url":"https://github.com/LiYinqi/DIVE"}],"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":1}}},{"leaderboard":"/sota/image-classification-on-plantdoc","slug":"image-classification-on-plantdoc","dataset":"PlantDoc","dataset_url":"/dataset/plantdoc","rows_in_archive":2,"metrics":["PARAMS","Accuracy"],"first_row_in_archive_order":{"model":"SCOLD","paper_title":"A Vision-Language Foundation Model for Leaf Disease Identification","paper_url":"/paper/a-vision-language-foundation-model-for-leaf","paper_date":"2025-05-11","arxiv_id":"2505.07019","code_links":[{"title":"enalis/scold","url":"https://huggingface.co/enalis/scold"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-plantvillage","slug":"image-classification-on-plantvillage","dataset":"PlantVillage","dataset_url":"/dataset/plantvillage","rows_in_archive":2,"metrics":["Accuracy","F1","Testing Ratio"],"first_row_in_archive_order":{"model":"adaptive minimal ensembling","paper_title":"Improving plant disease classification by adaptive minimal ensembling","paper_url":"/paper/improving-plant-disease-classification-by","paper_date":"2022-09-08","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-split-cifar-100","slug":"image-classification-on-split-cifar-100","dataset":"split CIFAR-100","dataset_url":null,"rows_in_archive":2,"metrics":["Average Accuracy","Percentage Average accuracy - 5 tasks"],"first_row_in_archive_order":{"model":"OFSCIL","paper_title":"12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning","paper_url":"/paper/12-mj-per-class-on-device-online-few-shot","paper_date":"2024-03-12","arxiv_id":"2403.07851","code_links":[{"title":"pulp-platform/fscil","url":"https://github.com/pulp-platform/fscil"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-webvision","slug":"image-classification-on-webvision","dataset":"WebVision","dataset_url":"/dataset/webvision-database","rows_in_archive":2,"metrics":["Top 1 Accuracy","Top 5 Accuracy"],"first_row_in_archive_order":{"model":"PropMix (Ours)","paper_title":"PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels","paper_url":"/paper/propmix-hard-sample-filtering-and","paper_date":"2021-10-22","arxiv_id":"2110.11809","code_links":[{"title":"filipe-research/propmix","url":"https://github.com/filipe-research/propmix"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-aider","slug":"image-classification-on-aider","dataset":"AIDER","dataset_url":"/dataset/aider","rows_in_archive":1,"metrics":["Test F1 score"],"first_row_in_archive_order":{"model":"TakuNet FP=16","paper_title":"TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response Scenarios","paper_url":"/paper/takunet-an-energy-efficient-cnn-for-real-time","paper_date":"2025-01-10","arxiv_id":"2501.05880","code_links":[{"title":"danielrossi1/takunet","url":"https://github.com/danielrossi1/takunet"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-aiderv2","slug":"image-classification-on-aiderv2","dataset":"AIDERV2","dataset_url":"/dataset/aiderv2","rows_in_archive":1,"metrics":["Test F1 score"],"first_row_in_archive_order":{"model":"TakuNet FP=16","paper_title":"TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response Scenarios","paper_url":"/paper/takunet-an-energy-efficient-cnn-for-real-time","paper_date":"2025-01-10","arxiv_id":"2501.05880","code_links":[{"title":"danielrossi1/takunet","url":"https://github.com/danielrossi1/takunet"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-amstertime","slug":"image-classification-on-amstertime","dataset":"AmsterTime","dataset_url":"/dataset/amstertime","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"AP-GeM (ResNet-101)","paper_title":"AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain Shift","paper_url":"/paper/amstertime-a-visual-place-recognition","paper_date":"2022-03-30","arxiv_id":"2203.16291","code_links":[{"title":"seyrankhademi/AmsterTime","url":"https://github.com/seyrankhademi/AmsterTime"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-artdl","slug":"image-classification-on-artdl","dataset":"ArtDL","dataset_url":"/dataset/artdl","rows_in_archive":1,"metrics":["Average Precision","F1"],"first_row_in_archive_order":{"model":"ResNet-50","paper_title":"A Data Set and a Convolutional Model for Iconography Classification in Paintings","paper_url":"/paper/a-data-set-and-a-convolutional-model-for","paper_date":"2020-10-06","arxiv_id":"2010.11697","code_links":[{"title":"iFede94/ArtDL","url":"https://github.com/iFede94/ArtDL"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-cars196","slug":"image-classification-on-cars196","dataset":"CARS196","dataset_url":"/dataset/cars196","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"µ2Net+ (ViT-L/16)","paper_title":"A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems","paper_url":"/paper/a-continual-development-methodology-for-large","paper_date":"2022-09-15","arxiv_id":"2209.07326","code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research/tree/master/muNet"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-cats-vs-dogs-1","slug":"image-classification-on-cats-vs-dogs-1","dataset":"cats_vs_dogs","dataset_url":"/dataset/cats-vs-dogs","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"µ2Net+ (ViT-L/16)","paper_title":"A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems","paper_url":"/paper/a-continual-development-methodology-for-large","paper_date":"2022-09-15","arxiv_id":"2209.07326","code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research/tree/master/muNet"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-chaoyang","slug":"image-classification-on-chaoyang","dataset":"Chaoyang","dataset_url":"/dataset/chaoyang","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"HSANR","paper_title":"Hard Sample Aware Noise Robust Learning for Histopathology Image Classification","paper_url":"/paper/hard-sample-aware-noise-robust-learning-for","paper_date":"2021-12-05","arxiv_id":"2112.03694","code_links":[{"title":"bupt-ai-cz/HSA-NRL","url":"https://github.com/bupt-ai-cz/HSA-NRL"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-cifar-100-60","slug":"image-classification-on-cifar-100-60","dataset":"CIFAR-100, 60% Symmetric Noise","dataset_url":null,"rows_in_archive":1,"metrics":["Percentage correct"],"first_row_in_archive_order":{"model":"MentorMix","paper_title":"Faster Meta Update Strategy for Noise-Robust Deep Learning","paper_url":"/paper/faster-meta-update-strategy-for-noise-robust","paper_date":"2021-04-30","arxiv_id":"2104.15092","code_links":[{"title":"youjiangxu/FaMUS","url":"https://github.com/youjiangxu/FaMUS"}],"syntology":{"n":7,"n_ran":2,"n_unverified":5,"n_pointer_only":7}}},{"leaderboard":"/sota/image-classification-on-cifar-100-alpha-0-20","slug":"image-classification-on-cifar-100-alpha-0-20","dataset":"CIFAR-100 (alpha=0, 20 clients per round)","dataset_url":null,"rows_in_archive":1,"metrics":["ACC@1-100Clients"],"first_row_in_archive_order":{"model":"FedAvgM + ASAM + SWA","paper_title":"Improving Generalization in Federated Learning by Seeking Flat Minima","paper_url":"/paper/improving-generalization-in-federated","paper_date":"2022-03-22","arxiv_id":"2203.11834","code_links":[{"title":"debcaldarola/fedsam","url":"https://github.com/debcaldarola/fedsam"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-cifar-100c","slug":"image-classification-on-cifar-100c","dataset":"CIFAR-100C","dataset_url":null,"rows_in_archive":1,"metrics":["Percentage correct"],"first_row_in_archive_order":{"model":"Astroformer","paper_title":"Astroformer: More Data Might not be all you need for Classification","paper_url":"/paper/astroformer-more-data-might-not-be-all-you","paper_date":"2023-04-03","arxiv_id":"2304.05350","code_links":[{"title":"Rishit-dagli/Astroformer","url":"https://github.com/Rishit-dagli/Astroformer"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-cifar-104000","slug":"image-classification-on-cifar-104000","dataset":"cifar-10,4000","dataset_url":"/dataset/cifar-10","rows_in_archive":1,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"WRN-28-2 + UDA+AutoDropout","paper_title":"AutoDropout: Learning Dropout Patterns to Regularize Deep Networks","paper_url":"/paper/autodropout-learning-dropout-patterns-to","paper_date":"2021-01-05","arxiv_id":"2101.01761","code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-cifar10-1","slug":"image-classification-on-cifar10-1","dataset":"cifar10","dataset_url":"/dataset/cifar-10","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"SAM","paper_title":null,"paper_url":null,"paper_date":"","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-cifar100","slug":"image-classification-on-cifar100","dataset":"cifar100","dataset_url":"/dataset/cifar-100","rows_in_archive":1,"metrics":["1:1 Accuracy"],"first_row_in_archive_order":{"model":"shreynet","paper_title":"Deep Residual Learning for Image Recognition","paper_url":"/paper/deep-residual-learning-for-image-recognition","paper_date":"2015-12-10","arxiv_id":"1512.03385","code_links":[{"title":"tensorflow/models","url":"https://github.com/tensorflow/models/tree/master/research/deeplab"},{"title":"tensorflow/models","url":"https://github.com/tensorflow/models"},{"title":"tensorflow/models","url":"https://github.com/tensorflow/models/tree/master/research/slim"},{"title":"labmlai/annotated_deep_learning_paper_implementations","url":"https://github.com/labmlai/annotated_deep_learning_paper_implementations"},{"title":"ultralytics/yolov5","url":"https://github.com/ultralytics/yolov5"},{"title":"JaidedAI/EasyOCR","url":"https://github.com/JaidedAI/EasyOCR"},{"title":"fastai/fastai","url":"https://github.com/fastai/fastai"},{"title":"pytorch/vision","url":"https://github.com/pytorch/vision"},{"title":"PaddlePaddle/PaddleDetection","url":"https://github.com/PaddlePaddle/PaddleDetection"},{"title":"fchollet/deep-learning-models","url":"https://github.com/fchollet/deep-learning-models"},{"title":"google/flax","url":"https://github.com/google/flax/tree/master/examples/imagenet"},{"title":"open-mmlab/mmpose","url":"https://github.com/open-mmlab/mmpose"},{"title":"KaimingHe/deep-residual-networks","url":"https://github.com/KaimingHe/deep-residual-networks"},{"title":"tensorpack/tensorpack","url":"https://github.com/tensorpack/tensorpack/tree/master/examples/ResNet"},{"title":"tensorpack/tensorpack","url":"https://github.com/tensorpack/tensorpack"},{"title":"yahoo/open_nsfw","url":"https://github.com/yahoo/open_nsfw"},{"title":"PaddlePaddle/PaddleClas","url":"https://github.com/PaddlePaddle/PaddleClas"},{"title":"Deci-AI/super-gradients","url":"https://github.com/Deci-AI/super-gradients"},{"title":"open-mmlab/mmclassification","url":"https://github.com/open-mmlab/mmclassification"},{"title":"towhee-io/towhee","url":"https://github.com/towhee-io/towhee"},{"title":"deepmind/dm-haiku","url":"https://github.com/deepmind/dm-haiku"},{"title":"osmr/imgclsmob","url":"https://github.com/osmr/imgclsmob"},{"title":"facebookarchive/fb.resnet.torch","url":"https://github.com/facebookarchive/fb.resnet.torch"},{"title":"facebook/fb.resnet.torch","url":"https://github.com/facebook/fb.resnet.torch"},{"title":"facebookresearch/pycls","url":"https://github.com/facebookresearch/pycls"},{"title":"qubvel/efficientnet","url":"https://github.com/qubvel/efficientnet"},{"title":"keras-team/keras-applications","url":"https://github.com/keras-team/keras-applications"},{"title":"alibaba/EasyCV","url":"https://github.com/alibaba/EasyCV"},{"title":"deepmodeling/deepmd-kit","url":"https://github.com/deepmodeling/deepmd-kit"},{"title":"ry/tensorflow-resnet","url":"https://github.com/ry/tensorflow-resnet"},{"title":"yoshitomo-matsubara/torchdistill","url":"https://github.com/yoshitomo-matsubara/torchdistill"},{"title":"facebookresearch/DomainBed","url":"https://github.com/facebookresearch/DomainBed"},{"title":"raghakot/keras-resnet","url":"https://github.com/raghakot/keras-resnet"},{"title":"akamaster/pytorch_resnet_cifar10","url":"https://github.com/akamaster/pytorch_resnet_cifar10"},{"title":"glouppe/info8010-deep-learning","url":"https://github.com/glouppe/info8010-deep-learning"},{"title":"bentrevett/pytorch-image-classification","url":"https://github.com/bentrevett/pytorch-image-classification"},{"title":"facebookresearch/hiera","url":"https://github.com/facebookresearch/hiera"},{"title":"HHTseng/video-classification","url":"https://github.com/HHTseng/video-classification"},{"title":"KaimingHe/resnet-1k-layers","url":"https://github.com/KaimingHe/resnet-1k-layers"},{"title":"NVIDIA/retinanet-examples","url":"https://github.com/NVIDIA/retinanet-examples"},{"title":"wenxinxu/resnet-in-tensorflow","url":"https://github.com/wenxinxu/resnet-in-tensorflow"},{"title":"wenxinxu/resnet_in_tensorflow","url":"https://github.com/wenxinxu/resnet_in_tensorflow"},{"title":"MishaLaskin/vqvae","url":"https://github.com/MishaLaskin/vqvae"},{"title":"MishaLaskin/vq-vae","url":"https://github.com/MishaLaskin/vq-vae"},{"title":"researchmm/SiamDW","url":"https://github.com/researchmm/SiamDW"},{"title":"sgrvinod/a-pytorch-tutorial-to-super-resolution","url":"https://github.com/sgrvinod/a-pytorch-tutorial-to-super-resolution"},{"title":"D-X-Y/ResNeXt-DenseNet","url":"https://github.com/D-X-Y/ResNeXt-DenseNet"},{"title":"hustvl/sparseinst","url":"https://github.com/hustvl/sparseinst"},{"title":"gcr/torch-residual-networks","url":"https://github.com/gcr/torch-residual-networks"},{"title":"xiaoyufenfei/LEDNet","url":"https://github.com/xiaoyufenfei/LEDNet"},{"title":"hsd1503/resnet1d","url":"https://github.com/hsd1503/resnet1d"},{"title":"kyle-dorman/bayesian-neural-network-blogpost","url":"https://github.com/kyle-dorman/bayesian-neural-network-blogpost"},{"title":"yxgeee/MMT","url":"https://github.com/yxgeee/MMT"},{"title":"andreasveit/densenet-pytorch","url":"https://github.com/andreasveit/densenet-pytorch"},{"title":"vietanhdev/open-adas","url":"https://github.com/vietanhdev/open-adas"},{"title":"jiweibo/imagenet","url":"https://github.com/jiweibo/imagenet"},{"title":"felixgwu/img_classification_pk_pytorch","url":"https://github.com/felixgwu/img_classification_pk_pytorch"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/research/cv/FaceAttribute"},{"title":"DeepMark/deepmark","url":"https://github.com/DeepMark/deepmark"},{"title":"itijyou/ademxapp","url":"https://github.com/itijyou/ademxapp"},{"title":"calmisential/TensorFlow2.0_ResNet","url":"https://github.com/calmisential/TensorFlow2.0_ResNet"},{"title":"j3soon/arxiv-utils","url":"https://github.com/j3soon/arxiv-utils"},{"title":"MegEngine/Models","url":"https://github.com/MegEngine/Models/tree/master/official/vision/classification/resnet"},{"title":"mahmoodlab/hest","url":"https://github.com/mahmoodlab/hest"},{"title":"peteryuX/arcface-tf2","url":"https://github.com/peteryuX/arcface-tf2"},{"title":"mindlab-ai/mindcv","url":"https://github.com/mindlab-ai/mindcv/blob/main/mindcv/models/resnet.py"},{"title":"mindspore-ecosystem/mindcv","url":"https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/resnet.py"},{"title":"kwotsin/TensorFlow-ENet","url":"https://github.com/kwotsin/TensorFlow-ENet"},{"title":"haonanguo/remote-sensing-chatgpt","url":"https://github.com/haonanguo/remote-sensing-chatgpt"},{"title":"hsinyilin19/resnetvae","url":"https://github.com/hsinyilin19/resnetvae"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/official/cv/resnet"},{"title":"Mayurji/Image-Classification-PyTorch","url":"https://github.com/Mayurji/Image-Classification-PyTorch"},{"title":"FrancescoSaverioZuppichini/ResNet","url":"https://github.com/FrancescoSaverioZuppichini/ResNet"},{"title":"EricAlcaide/MiniFold","url":"https://github.com/EricAlcaide/MiniFold"},{"title":"GKalliatakis/Keras-VGG16-places365","url":"https://github.com/GKalliatakis/Keras-VGG16-places365"},{"title":"OlafenwaMoses/IdenProf","url":"https://github.com/OlafenwaMoses/IdenProf"},{"title":"wondonghyeon/protest-detection-violence-estimation","url":"https://github.com/wondonghyeon/protest-detection-violence-estimation"},{"title":"SJTU-Thinklab-Det/r3det-on-mmdetection","url":"https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection"},{"title":"shijianjian/efficientnet-pytorch-3d","url":"https://github.com/shijianjian/efficientnet-pytorch-3d"},{"title":"facebookresearch/stochastic_gradient_push","url":"https://github.com/facebookresearch/stochastic_gradient_push"},{"title":"ndb796/pytorch-adversarial-training-cifar","url":"https://github.com/ndb796/pytorch-adversarial-training-cifar"},{"title":"paperswithcode/paperswithcode-client","url":"https://github.com/paperswithcode/paperswithcode-client"},{"title":"chrhenning/hypercl","url":"https://github.com/chrhenning/hypercl"},{"title":"OlafenwaMoses/Traffic-Net","url":"https://github.com/OlafenwaMoses/Traffic-Net"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/official/cv/resnet_thor"},{"title":"rtanno21609/AdaptiveNeuralTrees","url":"https://github.com/rtanno21609/AdaptiveNeuralTrees"},{"title":"hongyi-zhang/Fixup","url":"https://github.com/hongyi-zhang/Fixup"},{"title":"YudeWang/UNet-Satellite-Image-Segmentation","url":"https://github.com/YudeWang/UNet-Satellite-Image-Segmentation"},{"title":"deepquestai/fire-smoke-dataset","url":"https://github.com/deepquestai/fire-smoke-dataset"},{"title":"amazon-research/long-short-term-transformer","url":"https://github.com/amazon-research/long-short-term-transformer"},{"title":"amazon-science/long-short-term-transformer","url":"https://github.com/amazon-science/long-short-term-transformer"},{"title":"alecwangcq/KFAC-Pytorch","url":"https://github.com/alecwangcq/KFAC-Pytorch"},{"title":"makatx/YOLO_ResNet","url":"https://github.com/makatx/YOLO_ResNet"},{"title":"alrojo/lasagne_residual_network","url":"https://github.com/alrojo/lasagne_residual_network"},{"title":"alexsax/midlevel-reps","url":"https://github.com/alexsax/midlevel-reps"},{"title":"JayPatwardhan/ResNet-PyTorch","url":"https://github.com/JayPatwardhan/ResNet-PyTorch"},{"title":"marload/ConvNets-TensorFlow2","url":"https://github.com/marload/ConvNets-TensorFlow2"},{"title":"alvarobartt/serving-pytorch-models","url":"https://github.com/alvarobartt/serving-pytorch-models"},{"title":"yakhyo/gaze-estimation","url":"https://github.com/yakhyo/gaze-estimation"},{"title":"mingxingtan/efficientnet","url":"https://github.com/mingxingtan/efficientnet"},{"title":"sayakpaul/Adaptive-Gradient-Clipping","url":"https://github.com/sayakpaul/Adaptive-Gradient-Clipping"},{"title":"kamanphoebe/motiondetection","url":"https://github.com/kamanphoebe/motiondetection"},{"title":"canturan10/satellighte","url":"https://github.com/canturan10/satellighte"},{"title":"masoudnick/brain-tumor-mri-classification","url":"https://github.com/masoudnick/brain-tumor-mri-classification"},{"title":"mhagiwara/nanigonet","url":"https://github.com/mhagiwara/nanigonet"},{"title":"hycis/TensorGraph","url":"https://github.com/hycis/TensorGraph"},{"title":"oskyhn/CNNs-Without-Borders","url":"https://github.com/oskyhn/CNNs-Without-Borders"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/official/cv/resnet50_quant"},{"title":"waterdisappear/nudt4mstar","url":"https://github.com/waterdisappear/nudt4mstar"},{"title":"dnddnjs/pytorch-cifar10","url":"https://github.com/dnddnjs/pytorch-cifar10"},{"title":"Masao-Taketani/FOTS_OCR","url":"https://github.com/Masao-Taketani/FOTS_OCR"},{"title":"ChangweiZhang/Awesome-WindowsML-ONNX-Models","url":"https://github.com/ChangweiZhang/Awesome-WindowsML-ONNX-Models"},{"title":"OlafenwaMoses/Action-Net","url":"https://github.com/OlafenwaMoses/Action-Net"},{"title":"priyavrat-misra/xrays-and-gradcam","url":"https://github.com/priyavrat-misra/xrays-and-gradcam"},{"title":"MarkHershey/arxiv-dl","url":"https://github.com/MarkHershey/arxiv-dl"},{"title":"lixixibj/forecasting-with-time-series-imaging","url":"https://github.com/lixixibj/forecasting-with-time-series-imaging"},{"title":"lixixibj/tsImage-forecasting","url":"https://github.com/lixixibj/tsImage-forecasting"},{"title":"Lornatang/ResNet-PyTorch","url":"https://github.com/Lornatang/ResNet-PyTorch"},{"title":"Sakib1263/ResNet1D-Model-Builder-KERAS","url":"https://github.com/Sakib1263/ResNet1D-Model-Builder-KERAS"},{"title":"Sakib1263/ResNet-ResNetv2-ResNeXt-1D-2D-Tensorflow-Keras","url":"https://github.com/Sakib1263/ResNet-ResNetv2-ResNeXt-1D-2D-Tensorflow-Keras"},{"title":"ViswanathaReddyGajjala/EfficientNet-RetinaNet","url":"https://github.com/ViswanathaReddyGajjala/EfficientNet-RetinaNet"},{"title":"Sakib1263/1DResNet-Builder-KERAS","url":"https://github.com/Sakib1263/1DResNet-Builder-KERAS"},{"title":"Sakib1263/1DResNet-KERAS","url":"https://github.com/Sakib1263/1DResNet-KERAS"},{"title":"Sakib1263/ResNet-Model-Builder-KERAS","url":"https://github.com/Sakib1263/ResNet-Model-Builder-KERAS"},{"title":"Sakib1263/ResNet-Model-Builder-Tensorflow-Keras","url":"https://github.com/Sakib1263/ResNet-Model-Builder-Tensorflow-Keras"},{"title":"Sakib1263/ResNet-ResNeXt-1D-2D-Tensorflow-Keras","url":"https://github.com/Sakib1263/ResNet-ResNeXt-1D-2D-Tensorflow-Keras"},{"title":"yakhyo/head-pose-estimation","url":"https://github.com/yakhyo/head-pose-estimation"},{"title":"CVxTz/face_age_gender","url":"https://github.com/CVxTz/face_age_gender"},{"title":"deontaepharr/Residual-Attention-Network","url":"https://github.com/deontaepharr/Residual-Attention-Network"},{"title":"kmzzhang/nbi","url":"https://github.com/kmzzhang/nbi"},{"title":"uclaml/Padam","url":"https://github.com/uclaml/Padam"},{"title":"thughost2/Padam","url":"https://github.com/thughost2/Padam"},{"title":"barmayo/spatial_attention","url":"https://github.com/barmayo/spatial_attention"},{"title":"vlievin/Unet","url":"https://github.com/vlievin/Unet"},{"title":"Graylab/deepH3-distances-orientations","url":"https://github.com/Graylab/deepH3-distances-orientations"},{"title":"bethgelab/InDomainGeneralizationBenchmark","url":"https://github.com/bethgelab/InDomainGeneralizationBenchmark"},{"title":"km1414/CNN-models","url":"https://github.com/km1414/CNN-models"},{"title":"megvii-research/basecls","url":"https://github.com/megvii-research/basecls/tree/main/zoo/public/resnet"},{"title":"mobilesec/arcface-tensorflowlite","url":"https://github.com/mobilesec/arcface-tensorflowlite"},{"title":"hongkunsun/paratranscnn","url":"https://github.com/hongkunsun/paratranscnn"},{"title":"aby2s/sharpmask","url":"https://github.com/aby2s/sharpmask"},{"title":"k-miran/hear","url":"https://github.com/k-miran/hear"},{"title":"KellerJordan/ResNet-PyTorch-CIFAR10","url":"https://github.com/KellerJordan/ResNet-PyTorch-CIFAR10"},{"title":"sanghyun-son/clustering-kernels","url":"https://github.com/sanghyun-son/clustering-kernels"},{"title":"EstherBear/implementation-of-pruning-filters","url":"https://github.com/EstherBear/implementation-of-pruning-filters"},{"title":"winycg/MCL-OKD","url":"https://github.com/winycg/MCL-OKD"},{"title":"anguyen8/sam","url":"https://github.com/anguyen8/sam"},{"title":"the-super-toys/glimpse-models","url":"https://github.com/the-super-toys/glimpse-models"},{"title":"amogh7joshi/fer","url":"https://github.com/amogh7joshi/fer"},{"title":"amogh7joshi/engagement-detection","url":"https://github.com/amogh7joshi/engagement-detection"},{"title":"cornell-zhang/allo-pldi24-artifact","url":"https://github.com/cornell-zhang/allo-pldi24-artifact"},{"title":"yakhyo/retinaface-pytorch","url":"https://github.com/yakhyo/retinaface-pytorch"},{"title":"liuyao12/ConvNets-PDE-perspective","url":"https://github.com/liuyao12/ConvNets-PDE-perspective"},{"title":"DaikiTanak/manifold_mixup","url":"https://github.com/DaikiTanak/manifold_mixup"},{"title":"pszemraj/BoulderAreaDetector","url":"https://github.com/pszemraj/BoulderAreaDetector"},{"title":"johnnylu305/resnet-50-101-152","url":"https://github.com/johnnylu305/resnet-50-101-152"},{"title":"ycao5602/SAL","url":"https://github.com/ycao5602/SAL"},{"title":"swasun/VQ-VAE-images","url":"https://github.com/swasun/VQ-VAE-images"},{"title":"yudhisteer/Face-Recognition-with-Masks","url":"https://github.com/yudhisteer/Face-Recognition-with-Masks"},{"title":"PPPrior/i3d-pytorch","url":"https://github.com/PPPrior/i3d-pytorch"},{"title":"seyrankhademi/ResNet_CIFAR10","url":"https://github.com/seyrankhademi/ResNet_CIFAR10"},{"title":"birder/birder","url":"https://gitlab.com/birder/birder"},{"title":"JeyesHan/P2LR","url":"https://github.com/JeyesHan/P2LR"},{"title":"ljy-hy/mentormix_pytorch","url":"https://github.com/ljy-hy/mentormix_pytorch"},{"title":"hysts/pytorch_resnet","url":"https://github.com/hysts/pytorch_resnet"},{"title":"mbsariyildiz/resnet-pytorch","url":"https://github.com/mbsariyildiz/resnet-pytorch"},{"title":"usnistgov/image-regression-resnet50","url":"https://github.com/usnistgov/image-regression-resnet50"},{"title":"lab-midas/med_segmentation","url":"https://github.com/lab-midas/med_segmentation"},{"title":"koshian2/ResNet-MultipleFramework","url":"https://github.com/koshian2/ResNet-MultipleFramework"},{"title":"IBM/NeuronAlignment","url":"https://github.com/IBM/NeuronAlignment"},{"title":"Halesu/4th-ML100Days","url":"https://github.com/Halesu/4th-ML100Days"},{"title":"mindspore-courses/External-Attention-MindSpore","url":"https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/resnet.py"},{"title":"Sakib1263/DenseNet-1D-2D-Tensorflow-Keras","url":"https://github.com/Sakib1263/DenseNet-1D-2D-Tensorflow-Keras"},{"title":"DableUTeeF/keras-efficientnet","url":"https://github.com/DableUTeeF/keras-efficientnet"},{"title":"anoushkrit/Knowledge","url":"https://github.com/anoushkrit/Knowledge"},{"title":"anibali/dsnt-pose2d","url":"https://github.com/anibali/dsnt-pose2d"},{"title":"EdenMelaku/Transfer-Learning-Pytorch-Implmentation","url":"https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation"},{"title":"matakshay/Neural_Image_Caption_Generator","url":"https://github.com/matakshay/Neural_Image_Caption_Generator"},{"title":"EdenMelaku/Transfer-Learning-Pytorch-Implementation","url":"https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation"},{"title":"dmizr/phuber","url":"https://github.com/dmizr/phuber"},{"title":"vita-epfl/rock-pytorch","url":"https://github.com/vita-epfl/rock-pytorch"},{"title":"rajneeshaggarwal/google-efficientnet","url":"https://github.com/rajneeshaggarwal/google-efficientnet"},{"title":"isheunesutembo/TB-Computer-Aided-Diagnosis-Using-Deep-Learning","url":"https://github.com/isheunesutembo/TB-Computer-Aided-Diagnosis-Using-Deep-Learning"},{"title":"hysts/pytorch_resnet_preact","url":"https://github.com/hysts/pytorch_resnet_preact"},{"title":"chayanchatterjee/cbc-skynet","url":"https://github.com/chayanchatterjee/cbc-skynet"},{"title":"fL0n9/SKFAC-MindSpore","url":"https://github.com/fL0n9/SKFAC-MindSpore"},{"title":"chayanchatterjee/gw-skylocator","url":"https://github.com/chayanchatterjee/gw-skylocator"},{"title":"JONGSKY/paper","url":"https://github.com/JONGSKY/paper"},{"title":"ThanasisMattas/smartflow","url":"https://github.com/ThanasisMattas/smartflow"},{"title":"mindlab-ai/mindcv","url":"https://github.com/mindlab-ai/mindcv/blob/main/examples/train_cifar_dynamic.py"},{"title":"vineeths96/Gradient-Compression","url":"https://github.com/vineeths96/Gradient-Compression"},{"title":"rahul-t-p/ASVspoof-2019","url":"https://github.com/rahul-t-p/ASVspoof-2019"},{"title":"jung-jun-uk/mixface","url":"https://github.com/jung-jun-uk/mixface"},{"title":"asprenger/keras_fc_densenet","url":"https://github.com/asprenger/keras_fc_densenet"},{"title":"varshaneya/Res-SE-Net","url":"https://github.com/varshaneya/Res-SE-Net"},{"title":"microsoft/fb.resnet.torch","url":"https://github.com/microsoft/fb.resnet.torch"},{"title":"Thehunk1206/Covid-19-covidcnn","url":"https://github.com/Thehunk1206/Covid-19-covidcnn"},{"title":"c1ph3rr/Deep-Residual-Learning-for-Image-Recognition","url":"https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition"},{"title":"angelvillar96/FaceEmoji","url":"https://github.com/angelvillar96/FaceEmoji"},{"title":"moddy2024/resnet-9","url":"https://github.com/moddy2024/resnet-9"},{"title":"ajithvallabai/getsetgo_keras-beginner","url":"https://github.com/ajithvallabai/getsetgo_keras-beginner"},{"title":"Thehunk1206/Covid-19-chest-X-ray","url":"https://github.com/Thehunk1206/Covid-19-chest-X-ray"},{"title":"shtnkgm/VisionFrameworkSample","url":"https://github.com/shtnkgm/VisionFrameworkSample"},{"title":"greatsharma/DeepLearning-Papers-Implementation","url":"https://github.com/greatsharma/DeepLearning-Papers-Implementation"},{"title":"Duplums/bhb10k-dl-benchmark","url":"https://github.com/Duplums/bhb10k-dl-benchmark"},{"title":"abaietto/neural_ode_classification","url":"https://github.com/abaietto/neural_ode_classification"},{"title":"realmichaelye/Skin-Lesions-Classifier-ResNet50","url":"https://github.com/realmichaelye/Skin-Lesions-Classifier-ResNet50"},{"title":"kwantommy/fgvc6-kaggle-cassava-classification","url":"https://github.com/kwantommy/fgvc6-kaggle-cassava-classification"},{"title":"KarimaCha/CNN_Trust_Prediction","url":"https://github.com/KarimaCha/CNN_Trust_Prediction"},{"title":"MrRiahi/Convolutional-Neural-Networks-Tensorflow","url":"https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow"},{"title":"wuqiyao20160118/mini_project-for-CIFAR10","url":"https://github.com/wuqiyao20160118/mini_project-for-CIFAR10"},{"title":"xufanxiong/classification-of-caltech-256","url":"https://github.com/xufanxiong/classification-of-caltech-256"},{"title":"shoji9x9/CIFAR-10-By-small-ResNet","url":"https://github.com/shoji9x9/CIFAR-10-By-small-ResNet"},{"title":"DogeZen/resnet2onnx2tensorrt","url":"https://github.com/DogeZen/resnet2onnx2tensorrt"},{"title":"maxwelltsai/DeepGalaxy","url":"https://github.com/maxwelltsai/DeepGalaxy"},{"title":"iArunava/ResNet","url":"https://github.com/iArunava/ResNet"},{"title":"IMvision12/keras-vision-models","url":"https://github.com/IMvision12/keras-vision-models"},{"title":"akhadangi/EM-net","url":"https://github.com/akhadangi/EM-net"},{"title":"robertofranceschi/Image-classification-on-Caltech101-using-CNNs","url":"https://github.com/robertofranceschi/Image-classification-on-Caltech101-using-CNNs"},{"title":"usnistgov/image-classification-resnet50","url":"https://github.com/usnistgov/image-classification-resnet50"},{"title":"enzomuschik/distilfnd","url":"https://github.com/enzomuschik/distilfnd"},{"title":"MS-Mind/MS-Code-02","url":"https://github.com/MS-Mind/MS-Code-02/tree/main/configs/resnet"},{"title":"tanjeffreyz/deep-residual-learning","url":"https://github.com/tanjeffreyz/deep-residual-learning"},{"title":"xiuyu0000/papers_with_examples","url":"https://github.com/xiuyu0000/papers_with_examples/tree/main/cancer_detection_demo"},{"title":"fabiofumarola/ultrayolo","url":"https://github.com/fabiofumarola/ultrayolo"},{"title":"vineeths96/heterogeneous-systems","url":"https://github.com/vineeths96/heterogeneous-systems"},{"title":"shoji9x9/Fashion-MNIST-By-ResNet","url":"https://github.com/shoji9x9/Fashion-MNIST-By-ResNet"},{"title":"saikrishnadas/ResNet-Pytorch","url":"https://github.com/saikrishnadas/ResNet-Pytorch"},{"title":"Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation","url":"https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation"},{"title":"jimheaton/Ultra96_ML_Embedded_Workshop","url":"https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop"},{"title":"mindspore-courses/heads-on-mindspore","url":"https://github.com/mindspore-courses/heads-on-mindspore/tree/main/1-best-practice/models"},{"title":"sumitkutty/Anti-Spoof-Face-Recognition","url":"https://github.com/sumitkutty/Anti-Spoof-Face-Recognition"},{"title":"mdda/deep-learning-models","url":"https://github.com/mdda/deep-learning-models"},{"title":"qubvel/resnet_152","url":"https://github.com/qubvel/resnet_152"},{"title":"gpleiss/limits_of_large_width","url":"https://github.com/gpleiss/limits_of_large_width"},{"title":"jtcass01/SIMON","url":"https://github.com/jtcass01/SIMON"},{"title":"Baichenjia/Resnet","url":"https://github.com/Baichenjia/Resnet"},{"title":"MichaelWangfc/distributed-tensorflow-resnet","url":"https://github.com/MichaelWangfc/distributed-tensorflow-resnet"},{"title":"patientzero/timage-icann2019","url":"https://github.com/patientzero/timage-icann2019"},{"title":"aurelien-peden/Deep-Learning-paper-implementations","url":"https://github.com/aurelien-peden/Deep-Learning-paper-implementations"},{"title":"magnificent1208/r3det-on-mmdetection","url":"https://github.com/magnificent1208/r3det-on-mmdetection"},{"title":"ollewelin/libtorch-GPU-CNN-test-MNIST-with-Batchnorm","url":"https://github.com/ollewelin/libtorch-GPU-CNN-test-MNIST-with-Batchnorm"},{"title":"Anderies/stochastic-constant-pruninglayers","url":"https://github.com/Anderies/stochastic-constant-pruninglayers"},{"title":"jameswang287/Car-Detection","url":"https://github.com/jameswang287/Car-Detection"},{"title":"xinkuansong/modified-resnet-acc-0.9638-10.7M-parameters","url":"https://github.com/xinkuansong/modified-resnet-acc-0.9638-10.7M-parameters"},{"title":"arahosu/musicslots","url":"https://github.com/arahosu/musicslots"},{"title":"cjfghk5697/Pytorch-Research-Paper-Implementations","url":"https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations"},{"title":"serre-lab/gala_tpu","url":"https://github.com/serre-lab/gala_tpu"},{"title":"SarthakGarg13/OTOMYCOSIS","url":"https://github.com/SarthakGarg13/OTOMYCOSIS"},{"title":"sungyoonahn/Hint-based-image-colorization-using-Attention-Unet","url":"https://github.com/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet"},{"title":"uw-hai/limeade","url":"https://github.com/uw-hai/limeade"},{"title":"anjandeepsahni/face_classification","url":"https://github.com/anjandeepsahni/face_classification"},{"title":"pikkaay/efficientnet_gpu","url":"https://github.com/pikkaay/efficientnet_gpu"},{"title":"avocardio/resnet_vs_convnext","url":"https://github.com/avocardio/resnet_vs_convnext"},{"title":"code-implementation1/Code7","url":"https://github.com/code-implementation1/Code7/tree/main/resnetv2"},{"title":"Glp91/fire-detection","url":"https://github.com/Glp91/fire-detection"},{"title":"AngusG/bn-advex-zhang-fixup","url":"https://github.com/AngusG/bn-advex-zhang-fixup"},{"title":"danielamassiceti/CCA-visualdialogue","url":"https://github.com/danielamassiceti/CCA-visualdialogue"},{"title":"b8goal/PIRL-Residual-network","url":"https://github.com/b8goal/PIRL-Residual-network"},{"title":"alalagong/LEDNet","url":"https://github.com/alalagong/LEDNet"},{"title":"brycexu/MR-Residual-Net","url":"https://github.com/brycexu/MR-Residual-Net"},{"title":"shoaibahmed/CrossLayerPooling","url":"https://github.com/shoaibahmed/CrossLayerPooling"},{"title":"anjandeepsahni/face_recognition","url":"https://github.com/anjandeepsahni/face_recognition"},{"title":"sunnysoni97/single_image_fl","url":"https://github.com/sunnysoni97/single_image_fl"},{"title":"Bao-Jiarong/ResNet","url":"https://github.com/Bao-Jiarong/ResNet"},{"title":"sunlizhuang/YOLOv1-PaddlePaddle","url":"https://github.com/sunlizhuang/YOLOv1-PaddlePaddle"},{"title":"nicolalandro/Padam","url":"https://github.com/nicolalandro/Padam"},{"title":"yangorwell/NGPlus","url":"https://github.com/yangorwell/NGPlus"},{"title":"mindspore-courses/MindSpore-classification","url":"https://github.com/mindspore-courses/MindSpore-classification"},{"title":"kun-woo-park/Deeplearning_project_STL_10","url":"https://github.com/kun-woo-park/Deeplearning_project_STL_10"},{"title":"James-Gilbert-/pneumonia-detection","url":"https://github.com/James-Gilbert-/pneumonia-detection"},{"title":"vinod377/STN-OCR","url":"https://github.com/vinod377/STN-OCR"},{"title":"interestingzhuo/foodretreival","url":"https://github.com/interestingzhuo/foodretreival"},{"title":"godwinrayanc/YOLOv1-Pytorch","url":"https://github.com/godwinrayanc/YOLOv1-Pytorch"},{"title":"vinod377/STN-OCR-Tensorflow","url":"https://github.com/vinod377/STN-OCR-Tensorflow"},{"title":"2023-MindSpore-1/ms-code-217","url":"https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet_34"},{"title":"xhlulu/arxiv-assistant","url":"https://github.com/xhlulu/arxiv-assistant"},{"title":"sebsquire/Dogs-and-cats-image-classification-CNN","url":"https://github.com/sebsquire/Dogs-and-cats-image-classification-CNN"},{"title":"RobotMobile/cv-deep-learning-paper-review","url":"https://github.com/RobotMobile/cv-deep-learning-paper-review"},{"title":"zjZSTU/ResNet","url":"https://github.com/zjZSTU/ResNet"},{"title":"CocoSungMin/Gachon_SW_Colorization_Contest","url":"https://github.com/CocoSungMin/Gachon_SW_Colorization_Contest"},{"title":"rahulchaurasiya1/cautious-waddle","url":"https://github.com/rahulchaurasiya1/cautious-waddle"},{"title":"sidify/resnet_focal_loss","url":"https://github.com/sidify/resnet_focal_loss"},{"title":"Rakshit-Shetty/Resnet-Implementation","url":"https://github.com/Rakshit-Shetty/Resnet-Implementation"},{"title":"sebsquire/FlowersRecognition_keras","url":"https://github.com/sebsquire/FlowersRecognition_keras"},{"title":"musifahamran/FYP","url":"https://github.com/musifahamran/FYP"},{"title":"andrijdavid/clinical-brain-interface-2020","url":"https://github.com/andrijdavid/clinical-brain-interface-2020"},{"title":"2023-MindSpore-1/ms-code-217","url":"https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet34"},{"title":"kclip/localized-adaptive-risk-control","url":"https://github.com/kclip/localized-adaptive-risk-control"},{"title":"MindCode-4/code-4","url":"https://github.com/MindCode-4/code-4/tree/main/rembert"},{"title":"tiagoCuervo/JapaNet","url":"https://github.com/tiagoCuervo/JapaNet"},{"title":"ls-da3m0ns/Short-ResNet","url":"https://github.com/ls-da3m0ns/Short-ResNet"},{"title":"yakhyo/EfficientNet-PyTorch","url":"https://github.com/yakhyo/EfficientNet-PyTorch"},{"title":"fengredrum/cnn-xla","url":"https://github.com/fengredrum/cnn-xla"},{"title":"CryptoSalamander/pytorch_paper_implementation","url":"https://github.com/CryptoSalamander/pytorch_paper_implementation"},{"title":"James-Gilbert-/medical-deep-learning","url":"https://github.com/James-Gilbert-/medical-deep-learning"},{"title":"hershd23/ObjectLocalizer","url":"https://github.com/hershd23/ObjectLocalizer"},{"title":"imkoushik22/Walmart-Hackathon","url":"https://github.com/imkoushik22/Walmart-Hackathon"},{"title":"joeynavarro/facial_expression_classification","url":"https://github.com/joeynavarro/facial_expression_classification"},{"title":"2023-MindSpore-4/Code-5","url":"https://github.com/2023-MindSpore-4/Code-5/tree/main/lresnet100e_ir"},{"title":"alisha17/benchmarks","url":"https://github.com/alisha17/benchmarks"},{"title":"smc-x/ms-resnetv2","url":"https://github.com/smc-x/ms-resnetv2"},{"title":"dead-mazai/avia-missing-airplanes","url":"https://github.com/dead-mazai/avia-missing-airplanes"},{"title":"CellSMB/EM-net","url":"https://github.com/CellSMB/EM-net"},{"title":"konstantinos-p/image_classification_SOTA","url":"https://github.com/konstantinos-p/image_classification_SOTA"},{"title":"xcmyz/Face_Binary_Classfication","url":"https://github.com/xcmyz/Face_Binary_Classfication"},{"title":"julik43/IIMAS-USCS","url":"https://github.com/julik43/IIMAS-USCS"},{"title":"leemathew1998/RG","url":"https://github.com/leemathew1998/RG"},{"title":"JesonZhang822/Dogs-VS-Cats-Project","url":"https://github.com/JesonZhang822/Dogs-VS-Cats-Project"},{"title":"nsom/VGG16","url":"https://github.com/nsom/VGG16"},{"title":"phillity/BioCapsule","url":"https://github.com/phillity/BioCapsule"},{"title":"CodeChefVIT/VOID","url":"https://github.com/CodeChefVIT/VOID"},{"title":"xcmyz/FaceDetection","url":"https://github.com/xcmyz/FaceDetection"},{"title":"caojoshua/Plastic-Recyclability","url":"https://github.com/caojoshua/Plastic-Recyclability"},{"title":"jiajunhua/facebookresearch-Detectron","url":"https://github.com/jiajunhua/facebookresearch-Detectron"},{"title":"xcmyz/Face-Binary-Classification","url":"https://github.com/xcmyz/Face-Binary-Classification"},{"title":"yangboz/TransferLearning4Dentist","url":"https://github.com/yangboz/TransferLearning4Dentist"},{"title":"bclwan/MRI_Brain_Segmentation","url":"https://github.com/bclwan/MRI_Brain_Segmentation"},{"title":"VamsiKrishna1211/gender_age-detection","url":"https://github.com/VamsiKrishna1211/gender_age-detection"},{"title":"JanMarcelKezmann/Residual-Network-Architectures","url":"https://github.com/JanMarcelKezmann/Residual-Network-Architectures"},{"title":"aman-garg0001/fruit-classifier","url":"https://github.com/aman-garg0001/fruit-classifier"},{"title":"ashishpatel26/BrainMRI-Segmentation-Keras","url":"https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras"},{"title":"mminamina/Transfer_Learning_NaimishNet---Keypoints_Detection","url":"https://github.com/mminamina/Transfer_Learning_NaimishNet---Keypoints_Detection"},{"title":"mnguyen0226/image-augmentation-dnn-performance","url":"https://github.com/mnguyen0226/image-augmentation-dnn-performance"},{"title":"saloni1998/Cifar-10","url":"https://github.com/saloni1998/Cifar-10"},{"title":"SANKHA1/Vehicle-Detection","url":"https://github.com/SANKHA1/Vehicle-Detection"},{"title":"Burf/ResNet-Tensorflow2","url":"https://github.com/Burf/ResNet-Tensorflow2"},{"title":"AyushAniket/ResNET-and-various-normalizations","url":"https://github.com/AyushAniket/ResNET-and-various-normalizations"},{"title":"gatheluck/Stronghold","url":"https://github.com/gatheluck/Stronghold"},{"title":"cameronchoi/r3det-copy","url":"https://github.com/cameronchoi/r3det-copy"},{"title":"alililia/ascend_SE-Net","url":"https://github.com/alililia/ascend_SE-Net"},{"title":"kingcong/gpu_FaceAttribute","url":"https://github.com/kingcong/gpu_FaceAttribute"},{"title":"VhalPurohit/290s","url":"https://github.com/VhalPurohit/290s"},{"title":"northeastsquare/effficientnet","url":"https://github.com/northeastsquare/effficientnet"},{"title":"alililia/ms_extend","url":"https://github.com/alililia/ms_extend/tree/main/gpu_resnet"},{"title":"TowardHumanizedInteraction/TripletFace","url":"https://github.com/TowardHumanizedInteraction/TripletFace"},{"title":"CorneliusHsiao/FoodMethodGAN","url":"https://github.com/CorneliusHsiao/FoodMethodGAN"},{"title":"xiuyu0000/vision","url":"https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/resnet.py"},{"title":"xiuyu0000/tutorials","url":"https://github.com/xiuyu0000/tutorials/tree/main/resnet"},{"title":"pawelbeza/ClothesDetectorModel","url":"https://github.com/pawelbeza/ClothesDetectorModel"},{"title":"mminamina/Transfer_Learning_ResNet18---Keypoints_Detection","url":"https://github.com/mminamina/Transfer_Learning_ResNet18---Keypoints_Detection"},{"title":"Jeongyun-Lee-0423/Classification","url":"https://github.com/Jeongyun-Lee-0423/Classification"},{"title":"sammyamajumdar/ResNet50","url":"https://github.com/sammyamajumdar/ResNet50"},{"title":"LuigiRussoDev/Covid19Detection","url":"https://github.com/LuigiRussoDev/Covid19Detection"},{"title":"Mind23-2/MindCode-101","url":"https://github.com/Mind23-2/MindCode-101/tree/main/lresnet100e_ir"},{"title":"akommini/Spoken-Numeric-Digit-detection","url":"https://github.com/akommini/Spoken-Numeric-Digit-detection"},{"title":"demul/ResidualNet","url":"https://github.com/demul/ResidualNet"},{"title":"KellyHwong/rethinking_generalization","url":"https://github.com/KellyHwong/rethinking_generalization"},{"title":"NASA-NeMO-Net/NeMO-Net","url":"https://github.com/NASA-NeMO-Net/NeMO-Net"},{"title":"ntrin/CIFAR_10_ML","url":"https://github.com/ntrin/CIFAR_10_ML"},{"title":"axmoyal/Steganalysis-with-CNN","url":"https://github.com/axmoyal/Steganalysis-with-CNN"},{"title":"krishnakarthi/COVID-19_Prediction","url":"https://github.com/krishnakarthi/COVID-19_Prediction"},{"title":"alex-f1tor/Image-Caption","url":"https://github.com/alex-f1tor/Image-Caption"},{"title":"MioChiu/ResNet_TensorFlow","url":"https://github.com/MioChiu/ResNet_TensorFlow"},{"title":"vayuvegula/kerasModels","url":"https://github.com/vayuvegula/kerasModels"},{"title":"VinayBN8997/ResNet-CIFAR10","url":"https://github.com/VinayBN8997/ResNet-CIFAR10"},{"title":"malin9402/2020-0221","url":"https://github.com/malin9402/2020-0221"},{"title":"aniket-patra-1998/FLOWER_RECOGNITION","url":"https://github.com/aniket-patra-1998/FLOWER_RECOGNITION"},{"title":"kishanAk21/Skin-Cancer-Image-Classification","url":"https://github.com/kishanAk21/Skin-Cancer-Image-Classification"},{"title":"2023-MindSpore-4/Code10","url":"https://github.com/2023-MindSpore-4/Code10/tree/main/ResNet"},{"title":"Mind23-2/MindCode-3","url":"https://github.com/Mind23-2/MindCode-3/tree/main/lresnet100e_ir"},{"title":"2024-MindSpore-1/Code7","url":"https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet101"},{"title":"2024-MindSpore-1/Code7","url":"https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet50_v1"},{"title":"mabhay3420/Deep-Into-CNN","url":"https://github.com/mabhay3420/Deep-Into-CNN"},{"title":"swetak20/Deep_into_CNNs","url":"https://github.com/swetak20/Deep_into_CNNs"},{"title":"xslidi/EfficientNets_ddl_apex","url":"https://github.com/xslidi/EfficientNets_ddl_apex"},{"title":"mlvccn/bmtc_transferattackvid","url":"https://github.com/mlvccn/bmtc_transferattackvid"},{"title":"amogh7joshi/plant-health-detection","url":"https://github.com/amogh7joshi/plant-health-detection"},{"title":"HarmanDotpy/Normalizations-in-Deep-Learning","url":"https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning"},{"title":"fabxy/accessibilityCV","url":"https://github.com/fabxy/accessibilityCV"},{"title":"nsom/Basic-Models","url":"https://github.com/nsom/Basic-Models"},{"title":"wasilone11/ICPR-RIP-2024","url":"https://github.com/wasilone11/ICPR-RIP-2024"},{"title":"2023-MindSpore-1/ms-code-201","url":"https://github.com/2023-MindSpore-1/ms-code-201"},{"title":"2023-MindSpore-1/ms-code-213","url":"https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceAttribute"},{"title":"2023-MindSpore-1/ms-code-18","url":"https://github.com/2023-MindSpore-1/ms-code-18/tree/main/lresnet100e_ir"},{"title":"2023-MindSpore-1/ms-code-216","url":"https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnet50_adv_pruning"},{"title":"UnofficialJuliaMirrorSnapshots/DeepMark-deepmark","url":"https://github.com/UnofficialJuliaMirrorSnapshots/DeepMark-deepmark"},{"title":"seansoleyman/cifar10-resnet","url":"https://github.com/seansoleyman/cifar10-resnet"},{"title":"astoycos/Mini_Project2","url":"https://github.com/astoycos/Mini_Project2"},{"title":"Sangkwun/Resnet","url":"https://github.com/Sangkwun/Resnet"},{"title":"dssbxx/k_labels_resnet50","url":"https://github.com/dssbxx/k_labels_resnet50"},{"title":"hx19940102/Image-Segmentation-based-on-FCN","url":"https://github.com/hx19940102/Image-Segmentation-based-on-FCN"},{"title":"MindSpore-paper-code-3/code3","url":"https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceAttribute"},{"title":"CrossEntropy/Gesture-images-recognition-ResidualNetwork","url":"https://github.com/CrossEntropy/Gesture-images-recognition-ResidualNetwork"},{"title":"MindSpore-paper-code-2/code3","url":"https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_resnet34"},{"title":"ewinata/CIFAR-10","url":"https://github.com/ewinata/CIFAR-10"},{"title":"jday96314/Kuzushiji","url":"https://github.com/jday96314/Kuzushiji"},{"title":"johngear/eecs504","url":"https://github.com/johngear/eecs504"},{"title":"MindSpore-paper-code-3/code5","url":"https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet50_adv_pruning"},{"title":"yashclone999/ResNet_MODEL","url":"https://github.com/yashclone999/ResNet_MODEL"},{"title":"SiHaoShen/Convolutional-Neural-Networks","url":"https://github.com/SiHaoShen/Convolutional-Neural-Networks"},{"title":"code-implementation1/Code3","url":"https://github.com/code-implementation1/Code3/tree/main/FaceAttribute"},{"title":"MindSpore-paper-code-3/code7","url":"https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceAttribute"},{"title":"code-implementation1/Code5","url":"https://github.com/code-implementation1/Code5/tree/main/lresnet100e_ir"},{"title":"syiin/resnet_cancer_detection","url":"https://github.com/syiin/resnet_cancer_detection"},{"title":"oxygen0605/ImageClassification","url":"https://github.com/oxygen0605/ImageClassification"},{"title":"facundoq/transformational_measures_experiments","url":"https://github.com/facundoq/transformational_measures_experiments"},{"title":"yibeiou/Advanced_Business_Intelligence","url":"https://github.com/yibeiou/Advanced_Business_Intelligence"},{"title":"kiranu1024/deep-learning","url":"https://github.com/kiranu1024/deep-learning"},{"title":"lxy000719/ResNet-50-pneumonia-classification","url":"https://github.com/lxy000719/ResNet-50-pneumonia-classification"},{"title":"souravverma94/pre_trained_imgclassifier_dogBreeds","url":"https://github.com/souravverma94/pre_trained_imgclassifier_dogBreeds"},{"title":"maciejdomagala/DLWP_notes","url":"https://github.com/maciejdomagala/DLWP_notes"},{"title":"2023-MindSpore-4/Code7","url":"https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet34"},{"title":"Rohed/ml-1","url":"https://github.com/Rohed/ml-1"},{"title":"tcheung99/ResNet_MiniProject","url":"https://github.com/tcheung99/ResNet_MiniProject"},{"title":"JoeTracey/Portfolio","url":"https://github.com/JoeTracey/Portfolio"},{"title":"linainsaf/RESNET50_PNEUMONIA_DETECTION","url":"https://github.com/linainsaf/RESNET50_PNEUMONIA_DETECTION"},{"title":"ankit-vaghela30/Google-landmark-prediction","url":"https://github.com/ankit-vaghela30/Google-landmark-prediction"},{"title":"akar5h/Image-Recognition-with-CNN-and-Transfer-Learning-ResNet101","url":"https://github.com/akar5h/Image-Recognition-with-CNN-and-Transfer-Learning-ResNet101"},{"title":"trachpro/arcface-tf2","url":"https://github.com/trachpro/arcface-tf2"},{"title":"sangHa0411/CIFAR100","url":"https://github.com/sangHa0411/CIFAR100"},{"title":"alexwdong/IncubatorCVProject","url":"https://github.com/alexwdong/IncubatorCVProject"},{"title":"sangHa0411/ResNetCIFAR100","url":"https://github.com/sangHa0411/ResNetCIFAR100"},{"title":"JContro/pytorch_projects","url":"https://github.com/JContro/pytorch_projects"},{"title":"Vamshi399/CarND-Vehicle-Detection","url":"https://github.com/Vamshi399/CarND-Vehicle-Detection"},{"title":"3P2S/arcface","url":"https://github.com/3P2S/arcface"},{"title":"Olayemiy/Image-Classification-With-Resnet","url":"https://github.com/Olayemiy/Image-Classification-With-Resnet"},{"title":"adldotori/ResNet","url":"https://github.com/adldotori/ResNet"},{"title":"parviagrawal/IdenProf","url":"https://github.com/parviagrawal/IdenProf"},{"title":"eric-erki/IdenProf","url":"https://github.com/eric-erki/IdenProf"},{"title":"UnofficialJuliaMirror/DeepMark-deepmark","url":"https://github.com/UnofficialJuliaMirror/DeepMark-deepmark"},{"title":"cocopambag/ResNet","url":"https://github.com/cocopambag/ResNet"},{"title":"ariangc/breinchallenge","url":"https://github.com/ariangc/breinchallenge"},{"title":"MaxwellHeller/coffee_fraud_detection","url":"https://github.com/MaxwellHeller/coffee_fraud_detection"},{"title":"MiX-S/resnet_cifar10","url":"https://github.com/MiX-S/resnet_cifar10"},{"title":"paulblum00/ResNet-Recreation","url":"https://github.com/paulblum00/ResNet-Recreation"},{"title":"lixihan/Resnet","url":"https://github.com/lixihan/Resnet"},{"title":"SethEBaldwin/Resnet","url":"https://github.com/SethEBaldwin/Resnet"},{"title":"vinay-EIP/EIP-session-5-assignment","url":"https://github.com/vinay-EIP/EIP-session-5-assignment"},{"title":"seishinkikuchi/test","url":"https://github.com/seishinkikuchi/test"},{"title":"2023-MindSpore-4/Code7","url":"https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet_34"},{"title":"datoboat/Vehicle-Detection","url":"https://github.com/datoboat/Vehicle-Detection"},{"title":"gundeep15/SCD_AI_Project","url":"https://github.com/gundeep15/SCD_AI_Project"},{"title":"lkeonwoo94/DL_cv-mdt_NVIDIA_Cert_Course_StudyPI","url":"https://github.com/lkeonwoo94/DL_cv-mdt_NVIDIA_Cert_Course_StudyPI"},{"title":"hz2538/iot_JennyGo","url":"https://github.com/hz2538/iot_JennyGo"},{"title":"LuigiRussoDev/ResNets","url":"https://github.com/LuigiRussoDev/ResNets"},{"title":"boris127/vehicle-detection","url":"https://github.com/boris127/vehicle-detection"},{"title":"Zachdr1/CNNs","url":"https://github.com/Zachdr1/CNNs"},{"title":"horse007666/ResNet","url":"https://github.com/horse007666/ResNet"},{"title":"danielparada1/RL_SpaceInvaders","url":"https://github.com/danielparada1/RL_SpaceInvaders"},{"title":"kishorkuttan/traffic_net","url":"https://github.com/kishorkuttan/traffic_net"},{"title":"jvoynow/ConvNet-Architectures","url":"https://github.com/jvoynow/ConvNet-Architectures"},{"title":"sanoyo/dl-model","url":"https://github.com/sanoyo/dl-model"},{"title":"kingcong/resnet","url":"https://github.com/kingcong/resnet"},{"title":"yangyucheng000/resnet_Ascend","url":"https://github.com/yangyucheng000/resnet_Ascend"},{"title":"LKLQQ/ssc_resnet50","url":"https://github.com/LKLQQ/ssc_resnet50"},{"title":"alililia/ascend_FaceAttribute","url":"https://github.com/alililia/ascend_FaceAttribute"},{"title":"wangyuan249/Mymmt767","url":"https://github.com/wangyuan249/Mymmt767"},{"title":"manoranjan03/PaperImplementations","url":"https://github.com/manoranjan03/PaperImplementations"},{"title":"MindSpore-paper-code-3/code8","url":"https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceAttribute"},{"title":"kingcong/gpu_SE-Net","url":"https://github.com/kingcong/gpu_SE-Net"},{"title":"mr-bulb/DL_basic_github","url":"https://github.com/mr-bulb/DL_basic_github"},{"title":"ajaystar8/PDRUNet-PyTorch","url":"https://github.com/ajaystar8/PDRUNet-PyTorch"},{"title":"hustic/TinyImageSet","url":"https://github.com/hustic/TinyImageSet"},{"title":"2023-MindSpore-4/Code9","url":"https://github.com/2023-MindSpore-4/Code9/tree/main/ResNet"},{"title":"suhas1999/Flip-kart-grid-challenge","url":"https://github.com/suhas1999/Flip-kart-grid-challenge"},{"title":"abuchin/resnet_sign","url":"https://github.com/abuchin/resnet_sign"},{"title":"vietdhoang/resnet-18","url":"https://github.com/vietdhoang/resnet-18"},{"title":"JHLee0513/Salt_detection_challenge","url":"https://github.com/JHLee0513/Salt_detection_challenge"},{"title":"CanshangD/ResNet_tensorflow2.0","url":"https://github.com/CanshangD/ResNet_tensorflow2.0"},{"title":"syiin/ann_cancer_detection","url":"https://github.com/syiin/ann_cancer_detection"},{"title":"rickyHong/tfRecord-Caltech256-repl2","url":"https://github.com/rickyHong/tfRecord-Caltech256-repl2"},{"title":"vgangal101/resnet_models","url":"https://github.com/vgangal101/resnet_models"},{"title":"yw0nam/DenseNet","url":"https://github.com/yw0nam/DenseNet"},{"title":"2023-MindSpore-4/Code11","url":"https://github.com/2023-MindSpore-4/Code11/tree/main/resnet50_adv_pruning"},{"title":"rishabmallick/Sign-language-live-predictor","url":"https://github.com/rishabmallick/Sign-language-live-predictor"},{"title":"ben-davidson-6/fixup","url":"https://github.com/ben-davidson-6/fixup"},{"title":"duchaba/Norwegian_Blue_Parrot_k2fa_AI","url":"https://github.com/duchaba/Norwegian_Blue_Parrot_k2fa_AI"},{"title":"shao-chi/ImageCaption","url":"https://github.com/shao-chi/ImageCaption"},{"title":"mszpc/resnet18","url":"https://github.com/mszpc/resnet18"},{"title":"leemathew1998/GradientWeight","url":"https://github.com/leemathew1998/GradientWeight"},{"title":"mrzzy/np-dl-assign-1","url":"https://github.com/mrzzy/np-dl-assign-1"},{"title":"Mind23-2/MindCode-70","url":"https://github.com/Mind23-2/MindCode-70"},{"title":"2023-MindSpore-4/Code11","url":"https://github.com/2023-MindSpore-4/Code11/tree/main/ssc_resnet50"},{"title":"Mind23-2/MindCode-67","url":"https://github.com/Mind23-2/MindCode-67"},{"title":"cj-mclaughlin/segmentation_research","url":"https://github.com/cj-mclaughlin/segmentation_research"},{"title":"2023-MindSpore-1/ms-code-177","url":"https://github.com/2023-MindSpore-1/ms-code-177"},{"title":"2023-MindSpore-4/Code11","url":"https://github.com/2023-MindSpore-4/Code11/tree/main/ssd_resnet_34"},{"title":"2023-MindSpore-1/ms-code-22","url":"https://github.com/2023-MindSpore-1/ms-code-22"},{"title":"KatherineBaq/NMA-CN2021-fMRI_vs_DNNs","url":"https://github.com/KatherineBaq/NMA-CN2021-fMRI_vs_DNNs"},{"title":"bertrandlalo/deep_dog_breeds","url":"https://github.com/bertrandlalo/deep_dog_breeds"},{"title":"2023-MindSpore-1/ms-code-208","url":"https://github.com/2023-MindSpore-1/ms-code-208"},{"title":"t0930198/backup","url":"https://github.com/t0930198/backup"},{"title":"kaseris/ILSVRCPlus","url":"https://github.com/kaseris/ILSVRCPlus"}],"syntology":{"n":377,"n_ran":230,"n_unverified":147,"n_pointer_only":187}}},{"leaderboard":"/sota/image-classification-on-deep-pcb","slug":"image-classification-on-deep-pcb","dataset":"Deep PCB","dataset_url":"/dataset/deep-pcb","rows_in_archive":1,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"ResNet","paper_title":"Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation","paper_url":"/paper/improving-model-performance-and-removing-the","paper_date":"2022-05-01","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-dvs128-gesture","slug":"image-classification-on-dvs128-gesture","dataset":"DVS128 Gesture","dataset_url":"/dataset/dvs128-gesture-dataset","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"SNN","paper_title":"Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data","paper_url":"/paper/sneaky-spikes-uncovering-stealthy-backdoor","paper_date":"2023-02-13","arxiv_id":"2302.06279","code_links":[{"title":"GorkaAbad/Sneaky-Spikes","url":"https://github.com/GorkaAbad/Sneaky-Spikes"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-emnist-byclass","slug":"image-classification-on-emnist-byclass","dataset":"EMNIST-Byclass","dataset_url":"/dataset/emnist","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"WaveMixLite-128/7","paper_title":"WaveMix: A Resource-efficient Neural Network for Image Analysis","paper_url":"/paper/wavemix-lite-a-resource-efficient-neural","paper_date":"2022-05-28","arxiv_id":"2205.14375","code_links":[{"title":"pranavphoenix/WaveMix","url":"https://github.com/pranavphoenix/WaveMix"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-emnist-bymerge","slug":"image-classification-on-emnist-bymerge","dataset":"EMNIST-Bymerge","dataset_url":"/dataset/emnist","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"WaveMixLite-128/16","paper_title":"WaveMix: A Resource-efficient Neural Network for Image Analysis","paper_url":"/paper/wavemix-lite-a-resource-efficient-neural","paper_date":"2022-05-28","arxiv_id":"2205.14375","code_links":[{"title":"pranavphoenix/WaveMix","url":"https://github.com/pranavphoenix/WaveMix"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-esc-50","slug":"image-classification-on-esc-50","dataset":"ESC-50","dataset_url":"/dataset/esc-50","rows_in_archive":1,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":"SDGM-D","paper_title":"Performance of Gaussian Mixture Model Classifiers on Embedded Feature Spaces","paper_url":"/paper/performance-of-gaussian-mixture-model","paper_date":"2024-10-17","arxiv_id":"2410.13421","code_links":[{"title":"cvmlmu/dgmmc","url":"https://github.com/cvmlmu/dgmmc"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-femnist","slug":"image-classification-on-femnist","dataset":"FEMNIST","dataset_url":"/dataset/femnist","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"pFedBreD_ns_mg","paper_title":"Personalized Federated Learning with Hidden Information on Personalized Prior","paper_url":"/paper/personalized-federated-learning-with-hidden","paper_date":"2022-11-19","arxiv_id":"2211.10684","code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-fgvc-aircraft","slug":"image-classification-on-fgvc-aircraft","dataset":"FGVC Aircraft","dataset_url":"/dataset/fgvc-aircraft-1","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"TransBoost-ResNet50","paper_title":"TransBoost: Improving the Best ImageNet Performance using Deep Transduction","paper_url":"/paper/transboost-improving-the-best-imagenet","paper_date":"2022-05-26","arxiv_id":"2205.13331","code_links":[{"title":"omerb01/transboost","url":"https://github.com/omerb01/transboost"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-fgvc-aircraft-1","slug":"image-classification-on-fgvc-aircraft-1","dataset":"FGVC-Aircraft","dataset_url":"/dataset/fgvc-aircraft-1","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"EnGraf-Net101 (G=4, H=1)","paper_title":"EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task","paper_url":"/paper/engraf-net-multiple-granularity-branch","paper_date":"2021-10-21","arxiv_id":null,"code_links":[{"title":"artelabsuper/engraf-net","url":"https://gitlab.com/artelabsuper/engraf-net"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-flower102","slug":"image-classification-on-flower102","dataset":"Flower102","dataset_url":null,"rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"efficient adaptive ensembling","paper_title":"Efficient Adaptive Ensembling for Image Classification","paper_url":"/paper/efficient-adaptive-ensembling-for-image","paper_date":"2022-06-15","arxiv_id":"2206.07394","code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-flowers-tensorflow","slug":"image-classification-on-flowers-tensorflow","dataset":"Flowers (Tensorflow)","dataset_url":null,"rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"CNN+ Wilson-Cowan model RNN","paper_title":"Learning in Wilson-Cowan model for metapopulation","paper_url":"/paper/learning-in-wilson-cowan-model-for","paper_date":"2024-06-24","arxiv_id":"2406.16453","code_links":[{"title":"raffaelemarino/learning_in_wilsoncowan","url":"https://github.com/raffaelemarino/learning_in_wilsoncowan"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-fmd-texture","slug":"image-classification-on-fmd-texture","dataset":"FMD (materials)","dataset_url":"/dataset/fmd-texture","rows_in_archive":1,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"RADAM (ConvNeXt-L)","paper_title":"RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps","paper_url":"/paper/radam-texture-recognition-through-randomized-1","paper_date":"2023-03-08","arxiv_id":"2303.04554","code_links":[{"title":"scabini/RADAM","url":"https://github.com/scabini/RADAM"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-gtsrb","slug":"image-classification-on-gtsrb","dataset":"GTSRB","dataset_url":"/dataset/gtsrb","rows_in_archive":1,"metrics":["F1"],"first_row_in_archive_order":{"model":"SAG-ViT","paper_title":"SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers","paper_url":"/paper/sag-vit-a-scale-aware-high-fidelity-patching","paper_date":"2024-11-14","arxiv_id":"2411.09420","code_links":[{"title":"shravan-18/SAG-ViT","url":"https://github.com/shravan-18/SAG-ViT"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-icassava-19","slug":"image-classification-on-icassava-19","dataset":"iCassava'19","dataset_url":null,"rows_in_archive":1,"metrics":["Top-1 Accuracy"],"first_row_in_archive_order":{"model":"E2E-3M","paper_title":"Rethinking Recurrent Neural Networks and Other Improvements for Image Classification","paper_url":"/paper/rethinking-recurrent-neural-networks-and","paper_date":"2020-07-30","arxiv_id":"2007.15161","code_links":[{"title":"leonlha/e2e-3m","url":"https://github.com/leonlha/e2e-3m"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-imagenet-1","slug":"image-classification-on-imagenet-1","dataset":"delete","dataset_url":null,"rows_in_archive":1,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":null,"paper_title":"Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks","paper_url":"/paper/understanding-the-robustness-of-randomized","paper_date":"2023-10-01","arxiv_id":"2310.00567","code_links":[{"title":"mail-research/randomized_defenses","url":"https://github.com/mail-research/randomized_defenses"}],"syntology":{"n":20,"n_ran":15,"n_unverified":5,"n_pointer_only":20}}},{"leaderboard":"/sota/image-classification-on-imagenet-100-class-il","slug":"image-classification-on-imagenet-100-class-il","dataset":"ImageNet-100 (Class-IL, 5T)","dataset_url":null,"rows_in_archive":1,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":"MoCo + CaSSLe","paper_title":"Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning","paper_url":"/paper/sy-con-symmetric-contrastive-loss-for","paper_date":"2023-06-08","arxiv_id":"2306.05101","code_links":[{"title":"csm9493/PNR","url":"https://github.com/csm9493/PNR"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-imagenet-1k","slug":"image-classification-on-imagenet-1k","dataset":"imagenet-1k","dataset_url":"/dataset/imagenet","rows_in_archive":1,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":"BinaryViT","paper_title":"BinaryViT: Pushing Binary Vision Transformers Towards Convolutional Models","paper_url":"/paper/binaryvit-pushing-binary-vision-transformers","paper_date":"2023-06-29","arxiv_id":"2306.16678","code_links":[{"title":"phuoc-hoan-le/binaryvit","url":"https://github.com/phuoc-hoan-le/binaryvit"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-imagenet-32","slug":"image-classification-on-imagenet-32","dataset":"ImageNet-32","dataset_url":"/dataset/imagenet-32","rows_in_archive":1,"metrics":["Top 1 Error"],"first_row_in_archive_order":{"model":"WRN (N=28, k=10)","paper_title":"A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets","paper_url":"/paper/a-downsampled-variant-of-imagenet-as-an","paper_date":"2017-07-27","arxiv_id":"1707.08819","code_links":[{"title":"BayesWatch/cinic-10","url":"https://github.com/BayesWatch/cinic-10"},{"title":"PatrykChrabaszcz/Imagenet32_Scripts","url":"https://github.com/PatrykChrabaszcz/Imagenet32_Scripts"},{"title":"ZilinGao/GM-SOP","url":"https://github.com/ZilinGao/GM-SOP"},{"title":"Prev/downsampled-imagenet-path-fixer","url":"https://github.com/Prev/downsampled-imagenet-path-fixer"},{"title":"attaullah/Pretraining-WideResNet","url":"https://github.com/attaullah/Pretraining-WideResNet"},{"title":"curryandsun/AIOL","url":"https://github.com/curryandsun/AIOL"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-imagenet-64","slug":"image-classification-on-imagenet-64","dataset":"ImageNet-64","dataset_url":"/dataset/imagenet-64","rows_in_archive":1,"metrics":["Top 1 Error"],"first_row_in_archive_order":{"model":"WRN (N=36, k=5)","paper_title":"A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets","paper_url":"/paper/a-downsampled-variant-of-imagenet-as-an","paper_date":"2017-07-27","arxiv_id":"1707.08819","code_links":[{"title":"BayesWatch/cinic-10","url":"https://github.com/BayesWatch/cinic-10"},{"title":"PatrykChrabaszcz/Imagenet32_Scripts","url":"https://github.com/PatrykChrabaszcz/Imagenet32_Scripts"},{"title":"ZilinGao/GM-SOP","url":"https://github.com/ZilinGao/GM-SOP"},{"title":"Prev/downsampled-imagenet-path-fixer","url":"https://github.com/Prev/downsampled-imagenet-path-fixer"},{"title":"attaullah/Pretraining-WideResNet","url":"https://github.com/attaullah/Pretraining-WideResNet"},{"title":"curryandsun/AIOL","url":"https://github.com/curryandsun/AIOL"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-imagenet-9","slug":"image-classification-on-imagenet-9","dataset":"ImageNet-9","dataset_url":"/dataset/imagenet-9","rows_in_archive":1,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":"SqueezeNet + Simple Bypass","paper_title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","paper_url":"/paper/squeezenet-alexnet-level-accuracy-with-50x","paper_date":"2016-02-24","arxiv_id":"1602.07360","code_links":[{"title":"pytorch/vision","url":"https://github.com/pytorch/vision"},{"title":"PaddlePaddle/PaddleClas","url":"https://github.com/PaddlePaddle/PaddleClas"},{"title":"osmr/imgclsmob","url":"https://github.com/osmr/imgclsmob"},{"title":"DeepScale/SqueezeNet","url":"https://github.com/DeepScale/SqueezeNet"},{"title":"jiweibo/imagenet","url":"https://github.com/jiweibo/imagenet"},{"title":"rcmalli/keras-squeezenet","url":"https://github.com/rcmalli/keras-squeezenet"},{"title":"songhan/SqueezeNet-Deep-Compression","url":"https://github.com/songhan/SqueezeNet-Deep-Compression"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/research/cv/squeezenet"},{"title":"mindspore-ecosystem/mindcv","url":"https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/squeezenet.py"},{"title":"mindlab-ai/mindcv","url":"https://github.com/mindlab-ai/mindcv/blob/main/mindcv/models/squeezenet.py"},{"title":"DT42/squeezenet_demo","url":"https://github.com/DT42/squeezenet_demo"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/official/cv/squeezenet"},{"title":"Mayurji/Image-Classification-PyTorch","url":"https://github.com/Mayurji/Image-Classification-PyTorch"},{"title":"Element-Research/dpnn","url":"https://github.com/Element-Research/dpnn"},{"title":"dividiti/ck-caffe","url":"https://github.com/dividiti/ck-caffe"},{"title":"vonclites/squeezenet","url":"https://github.com/vonclites/squeezenet"},{"title":"marload/ConvNets-TensorFlow2","url":"https://github.com/marload/ConvNets-TensorFlow2"},{"title":"gsp-27/pytorch_Squeezenet","url":"https://github.com/gsp-27/pytorch_Squeezenet"},{"title":"lizeng614/SqueezeNet-Neural-Style-Pytorch","url":"https://github.com/lizeng614/SqueezeNet-Neural-Style-Pytorch"},{"title":"mtmd/Mobile_ConvNet","url":"https://github.com/mtmd/Mobile_ConvNet"},{"title":"Kaido0/Brain-Tissue-Segment-Keras","url":"https://github.com/Kaido0/Brain-Tissue-Segment-Keras"},{"title":"matteo-rizzo/fc4-pytorch","url":"https://github.com/matteo-rizzo/fc4-pytorch"},{"title":"avoroshilov/tf-squeezenet","url":"https://github.com/avoroshilov/tf-squeezenet"},{"title":"ejlb/squeezenet-chainer","url":"https://github.com/ejlb/squeezenet-chainer"},{"title":"haria/SqueezeNet","url":"https://github.com/haria/SqueezeNet"},{"title":"cmasch/squeezenet","url":"https://github.com/cmasch/squeezenet"},{"title":"milliemince/eBay-shipping-predictions","url":"https://github.com/milliemince/eBay-shipping-predictions"},{"title":"birder/birder","url":"https://gitlab.com/birder/birder"},{"title":"Dawars/SqueezeNet-tf","url":"https://github.com/Dawars/SqueezeNet-tf"},{"title":"Banus/caffe-demo","url":"https://github.com/Banus/caffe-demo"},{"title":"zjZSTU/LightWeightCNN","url":"https://github.com/zjZSTU/LightWeightCNN"},{"title":"deep-learning-algorithm/LightWeightCNN","url":"https://github.com/deep-learning-algorithm/LightWeightCNN"},{"title":"KentaItakura/Classify-crack-image-and-explain-why-using-MATLAB","url":"https://github.com/KentaItakura/Classify-crack-image-and-explain-why-using-MATLAB"},{"title":"mindspore-courses/heads-on-mindspore","url":"https://github.com/mindspore-courses/heads-on-mindspore/blob/main/1-best-practice/models"},{"title":"MS-Mind/MS-Code-02","url":"https://github.com/MS-Mind/MS-Code-02/tree/main/configs/squeezenet"},{"title":"MrRen-sdhm/Embedded_Multi_Object_Detection_CNN","url":"https://github.com/MrRen-sdhm/Embedded_Multi_Object_Detection_CNN"},{"title":"King-Otaku/Emotion_Recognition_DNN","url":"https://github.com/King-Otaku/Emotion_Recognition_DNN"},{"title":"xin-w8023/SqueezeNet-PyTorch","url":"https://github.com/xin-w8023/SqueezeNet-PyTorch"},{"title":"Qengineering/SqueezeNet-ncnn","url":"https://github.com/Qengineering/SqueezeNet-ncnn"},{"title":"m1lhaus/SimpleSqueezeNet","url":"https://github.com/m1lhaus/SimpleSqueezeNet"},{"title":"2023-MindSpore-1/ms-code-217","url":"https://github.com/2023-MindSpore-1/ms-code-217/tree/main/squeezenet"},{"title":"brianjychan/landuse","url":"https://github.com/brianjychan/landuse"},{"title":"AlexandruBurlacu/keras_squeezenet","url":"https://github.com/AlexandruBurlacu/keras_squeezenet"},{"title":"taltole/LiteNetwork_Image_Classification","url":"https://github.com/taltole/LiteNetwork_Image_Classification"},{"title":"taltole/CIFAR10_SqueezeNet","url":"https://github.com/taltole/CIFAR10_SqueezeNet"},{"title":"maxemerling/COVID_CT","url":"https://github.com/maxemerling/COVID_CT"},{"title":"Mind23-2/MindCode-116","url":"https://github.com/Mind23-2/MindCode-116"},{"title":"2023-MindSpore-4/Code11","url":"https://github.com/2023-MindSpore-4/Code11/tree/main/squeezenet"},{"title":"adeely9/experiment_2_python3","url":"https://github.com/adeely9/experiment_2_python3"},{"title":"vibhu444/alexnet-squeeze-mnist","url":"https://github.com/vibhu444/alexnet-squeeze-mnist"},{"title":"bogireddytejareddy/3d-squeezenet","url":"https://github.com/bogireddytejareddy/3d-squeezenet"},{"title":"code-implementation1/Code8","url":"https://github.com/code-implementation1/Code8/tree/main/squeezenet"},{"title":"mdsarfarazulh/fire-module","url":"https://github.com/mdsarfarazulh/fire-module"},{"title":"johngear/eecs504","url":"https://github.com/johngear/eecs504"},{"title":"Jastot/Rodinka_Neural_Network","url":"https://github.com/Jastot/Rodinka_Neural_Network"},{"title":"kingcong/squeezenet","url":"https://github.com/kingcong/squeezenet"},{"title":"Goandwanderfaraway/squeezenet-mindspore","url":"https://github.com/Goandwanderfaraway/squeezenet-mindspore"},{"title":"modelhub-ai/squeezenet","url":"https://github.com/modelhub-ai/squeezenet"},{"title":"Mind23-2/MindCode-80","url":"https://github.com/Mind23-2/MindCode-80"}],"syntology":{"n":4,"n_ran":4,"n_unverified":0,"n_pointer_only":2}}},{"leaderboard":"/sota/image-classification-on-imagenet-p","slug":"image-classification-on-imagenet-p","dataset":"ImageNet-P","dataset_url":"/dataset/imagenet-p","rows_in_archive":1,"metrics":["Top 5 Accuracy"],"first_row_in_archive_order":{"model":"SqueezeNet + Simple Bypass","paper_title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","paper_url":"/paper/squeezenet-alexnet-level-accuracy-with-50x","paper_date":"2016-02-24","arxiv_id":"1602.07360","code_links":[{"title":"pytorch/vision","url":"https://github.com/pytorch/vision"},{"title":"PaddlePaddle/PaddleClas","url":"https://github.com/PaddlePaddle/PaddleClas"},{"title":"osmr/imgclsmob","url":"https://github.com/osmr/imgclsmob"},{"title":"DeepScale/SqueezeNet","url":"https://github.com/DeepScale/SqueezeNet"},{"title":"jiweibo/imagenet","url":"https://github.com/jiweibo/imagenet"},{"title":"rcmalli/keras-squeezenet","url":"https://github.com/rcmalli/keras-squeezenet"},{"title":"songhan/SqueezeNet-Deep-Compression","url":"https://github.com/songhan/SqueezeNet-Deep-Compression"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/research/cv/squeezenet"},{"title":"mindspore-ecosystem/mindcv","url":"https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/squeezenet.py"},{"title":"mindlab-ai/mindcv","url":"https://github.com/mindlab-ai/mindcv/blob/main/mindcv/models/squeezenet.py"},{"title":"DT42/squeezenet_demo","url":"https://github.com/DT42/squeezenet_demo"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/official/cv/squeezenet"},{"title":"Mayurji/Image-Classification-PyTorch","url":"https://github.com/Mayurji/Image-Classification-PyTorch"},{"title":"Element-Research/dpnn","url":"https://github.com/Element-Research/dpnn"},{"title":"dividiti/ck-caffe","url":"https://github.com/dividiti/ck-caffe"},{"title":"vonclites/squeezenet","url":"https://github.com/vonclites/squeezenet"},{"title":"marload/ConvNets-TensorFlow2","url":"https://github.com/marload/ConvNets-TensorFlow2"},{"title":"gsp-27/pytorch_Squeezenet","url":"https://github.com/gsp-27/pytorch_Squeezenet"},{"title":"lizeng614/SqueezeNet-Neural-Style-Pytorch","url":"https://github.com/lizeng614/SqueezeNet-Neural-Style-Pytorch"},{"title":"mtmd/Mobile_ConvNet","url":"https://github.com/mtmd/Mobile_ConvNet"},{"title":"Kaido0/Brain-Tissue-Segment-Keras","url":"https://github.com/Kaido0/Brain-Tissue-Segment-Keras"},{"title":"matteo-rizzo/fc4-pytorch","url":"https://github.com/matteo-rizzo/fc4-pytorch"},{"title":"avoroshilov/tf-squeezenet","url":"https://github.com/avoroshilov/tf-squeezenet"},{"title":"ejlb/squeezenet-chainer","url":"https://github.com/ejlb/squeezenet-chainer"},{"title":"haria/SqueezeNet","url":"https://github.com/haria/SqueezeNet"},{"title":"cmasch/squeezenet","url":"https://github.com/cmasch/squeezenet"},{"title":"milliemince/eBay-shipping-predictions","url":"https://github.com/milliemince/eBay-shipping-predictions"},{"title":"birder/birder","url":"https://gitlab.com/birder/birder"},{"title":"Dawars/SqueezeNet-tf","url":"https://github.com/Dawars/SqueezeNet-tf"},{"title":"Banus/caffe-demo","url":"https://github.com/Banus/caffe-demo"},{"title":"zjZSTU/LightWeightCNN","url":"https://github.com/zjZSTU/LightWeightCNN"},{"title":"deep-learning-algorithm/LightWeightCNN","url":"https://github.com/deep-learning-algorithm/LightWeightCNN"},{"title":"KentaItakura/Classify-crack-image-and-explain-why-using-MATLAB","url":"https://github.com/KentaItakura/Classify-crack-image-and-explain-why-using-MATLAB"},{"title":"mindspore-courses/heads-on-mindspore","url":"https://github.com/mindspore-courses/heads-on-mindspore/blob/main/1-best-practice/models"},{"title":"MS-Mind/MS-Code-02","url":"https://github.com/MS-Mind/MS-Code-02/tree/main/configs/squeezenet"},{"title":"MrRen-sdhm/Embedded_Multi_Object_Detection_CNN","url":"https://github.com/MrRen-sdhm/Embedded_Multi_Object_Detection_CNN"},{"title":"King-Otaku/Emotion_Recognition_DNN","url":"https://github.com/King-Otaku/Emotion_Recognition_DNN"},{"title":"xin-w8023/SqueezeNet-PyTorch","url":"https://github.com/xin-w8023/SqueezeNet-PyTorch"},{"title":"Qengineering/SqueezeNet-ncnn","url":"https://github.com/Qengineering/SqueezeNet-ncnn"},{"title":"m1lhaus/SimpleSqueezeNet","url":"https://github.com/m1lhaus/SimpleSqueezeNet"},{"title":"2023-MindSpore-1/ms-code-217","url":"https://github.com/2023-MindSpore-1/ms-code-217/tree/main/squeezenet"},{"title":"brianjychan/landuse","url":"https://github.com/brianjychan/landuse"},{"title":"AlexandruBurlacu/keras_squeezenet","url":"https://github.com/AlexandruBurlacu/keras_squeezenet"},{"title":"taltole/LiteNetwork_Image_Classification","url":"https://github.com/taltole/LiteNetwork_Image_Classification"},{"title":"taltole/CIFAR10_SqueezeNet","url":"https://github.com/taltole/CIFAR10_SqueezeNet"},{"title":"maxemerling/COVID_CT","url":"https://github.com/maxemerling/COVID_CT"},{"title":"Mind23-2/MindCode-116","url":"https://github.com/Mind23-2/MindCode-116"},{"title":"2023-MindSpore-4/Code11","url":"https://github.com/2023-MindSpore-4/Code11/tree/main/squeezenet"},{"title":"adeely9/experiment_2_python3","url":"https://github.com/adeely9/experiment_2_python3"},{"title":"vibhu444/alexnet-squeeze-mnist","url":"https://github.com/vibhu444/alexnet-squeeze-mnist"},{"title":"bogireddytejareddy/3d-squeezenet","url":"https://github.com/bogireddytejareddy/3d-squeezenet"},{"title":"code-implementation1/Code8","url":"https://github.com/code-implementation1/Code8/tree/main/squeezenet"},{"title":"mdsarfarazulh/fire-module","url":"https://github.com/mdsarfarazulh/fire-module"},{"title":"johngear/eecs504","url":"https://github.com/johngear/eecs504"},{"title":"Jastot/Rodinka_Neural_Network","url":"https://github.com/Jastot/Rodinka_Neural_Network"},{"title":"kingcong/squeezenet","url":"https://github.com/kingcong/squeezenet"},{"title":"Goandwanderfaraway/squeezenet-mindspore","url":"https://github.com/Goandwanderfaraway/squeezenet-mindspore"},{"title":"modelhub-ai/squeezenet","url":"https://github.com/modelhub-ai/squeezenet"},{"title":"Mind23-2/MindCode-80","url":"https://github.com/Mind23-2/MindCode-80"}],"syntology":{"n":4,"n_ran":4,"n_unverified":0,"n_pointer_only":2}}},{"leaderboard":"/sota/image-classification-on-imagenet-sketch","slug":"image-classification-on-imagenet-sketch","dataset":"ImageNet-Sketch","dataset_url":"/dataset/imagenet-sketch","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"µ2Net+ (ViT-L/16)","paper_title":"A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems","paper_url":"/paper/a-continual-development-methodology-for-large","paper_date":"2022-09-15","arxiv_id":"2209.07326","code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research/tree/master/muNet"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-inat2021-mini","slug":"image-classification-on-inat2021-mini","dataset":"iNat2021-mini","dataset_url":"/dataset/inaturalist","rows_in_archive":1,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":"WaveMix-256/16 (level 2)","paper_title":"WaveMix: A Resource-efficient Neural Network for Image Analysis","paper_url":"/paper/wavemix-lite-a-resource-efficient-neural","paper_date":"2022-05-28","arxiv_id":"2205.14375","code_links":[{"title":"pranavphoenix/WaveMix","url":"https://github.com/pranavphoenix/WaveMix"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-isbnet","slug":"image-classification-on-isbnet","dataset":"ISBNet","dataset_url":"/dataset/isbnet","rows_in_archive":1,"metrics":["Macro F1"],"first_row_in_archive_order":{"model":"ThanosNet","paper_title":"ThanosNet: A Novel Trash Classification Method Using Metadata","paper_url":"/paper/thanosnet-a-novel-trash-classification-method","paper_date":"2021-03-19","arxiv_id":null,"code_links":[{"title":"alansun17904/smart-trash","url":"https://github.com/alansun17904/smart-trash"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-kitti-dist","slug":"image-classification-on-kitti-dist","dataset":"KITTI-Dist","dataset_url":null,"rows_in_archive":1,"metrics":["Top 1 Accuracy"],"first_row_in_archive_order":{"model":"SEER (RegNet10B)","paper_title":"Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision","paper_url":"/paper/vision-models-are-more-robust-and-fair-when","paper_date":"2022-02-16","arxiv_id":"2202.08360","code_links":[{"title":"facebookresearch/vissl","url":"https://github.com/facebookresearch/vissl"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-kmnist","slug":"image-classification-on-kmnist","dataset":"KMNIST","dataset_url":"/dataset/kmnist","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"µ2Net (ViT-L/16)","paper_title":"An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems","paper_url":"/paper/an-evolutionary-approach-to-dynamic","paper_date":"2022-05-25","arxiv_id":"2205.12755","code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research/tree/master/muNet"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-kth-tips2","slug":"image-classification-on-kth-tips2","dataset":"KTH-TIPS2","dataset_url":"/dataset/kth-tips2","rows_in_archive":1,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"RADAM (ConvNeXt-XL)","paper_title":"RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps","paper_url":"/paper/radam-texture-recognition-through-randomized-1","paper_date":"2023-03-08","arxiv_id":"2303.04554","code_links":[{"title":"scabini/RADAM","url":"https://github.com/scabini/RADAM"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-labelme","slug":"image-classification-on-labelme","dataset":"LabelMe","dataset_url":"/dataset/labelme","rows_in_archive":1,"metrics":["Test Accuracy"],"first_row_in_archive_order":{"model":"CoNAL","paper_title":"Learning from Crowds by Modeling Common Confusions","paper_url":"/paper/learning-from-crowds-by-modeling-common","paper_date":"2020-12-24","arxiv_id":"2012.13052","code_links":[{"title":"seunghyukcho/CoNAL-pytorch","url":"https://github.com/seunghyukcho/CoNAL-pytorch"},{"title":"seunghyukcho/doctornet-pytorch","url":"https://github.com/seunghyukcho/doctornet-pytorch"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-leafnet","slug":"image-classification-on-leafnet","dataset":"LeafNet","dataset_url":"/dataset/leafnet","rows_in_archive":1,"metrics":["Accuracy (Top-1)"],"first_row_in_archive_order":{"model":"SCOLD","paper_title":"A Vision-Language Foundation Model for Leaf Disease Identification","paper_url":"/paper/a-vision-language-foundation-model-for-leaf","paper_date":"2025-05-11","arxiv_id":"2505.07019","code_links":[{"title":"enalis/scold","url":"https://huggingface.co/enalis/scold"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-mnist-1","slug":"image-classification-on-mnist-1","dataset":"mnist","dataset_url":"/dataset/mnist","rows_in_archive":1,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"WaveMixLite","paper_title":"WaveMix: A Resource-efficient Neural Network for Image Analysis","paper_url":"/paper/wavemix-lite-a-resource-efficient-neural","paper_date":"2022-05-28","arxiv_id":"2205.14375","code_links":[{"title":"pranavphoenix/WaveMix","url":"https://github.com/pranavphoenix/WaveMix"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-mnist-rot-12","slug":"image-classification-on-mnist-rot-12","dataset":"MNIST-rot-12","dataset_url":null,"rows_in_archive":1,"metrics":["Test Error"],"first_row_in_archive_order":{"model":"PDO-eConv (ours)","paper_title":"PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions","paper_url":"/paper/pdo-econvs-partial-differential-operator","paper_date":"2020-07-20","arxiv_id":"2007.10408","code_links":[{"title":"shenzy08/PDO-eConvs","url":"https://github.com/shenzy08/PDO-eConvs"},{"title":"Roderickzzc/Pdo-econv-pytorch","url":"https://github.com/Roderickzzc/Pdo-econv-pytorch"},{"title":"ejnnr/steerable_pdo_experiments","url":"https://github.com/ejnnr/steerable_pdo_experiments"}],"syntology":{"n":7,"n_ran":0,"n_unverified":7,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-mnist-rot-12k-da","slug":"image-classification-on-mnist-rot-12k-da","dataset":"MNIST-rot-12k (DA)","dataset_url":null,"rows_in_archive":1,"metrics":["Test Error"],"first_row_in_archive_order":{"model":"PDO-eConv (ours)","paper_title":"PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions","paper_url":"/paper/pdo-econvs-partial-differential-operator","paper_date":"2020-07-20","arxiv_id":"2007.10408","code_links":[{"title":"shenzy08/PDO-eConvs","url":"https://github.com/shenzy08/PDO-eConvs"},{"title":"Roderickzzc/Pdo-econv-pytorch","url":"https://github.com/Roderickzzc/Pdo-econv-pytorch"},{"title":"ejnnr/steerable_pdo_experiments","url":"https://github.com/ejnnr/steerable_pdo_experiments"}],"syntology":{"n":7,"n_ran":0,"n_unverified":7,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-multimnist","slug":"image-classification-on-multimnist","dataset":"MultiMNIST","dataset_url":"/dataset/multimnist","rows_in_archive":1,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"CapsNet","paper_title":"Dynamic Routing Between Capsules","paper_url":"/paper/dynamic-routing-between-capsules","paper_date":"2017-10-26","arxiv_id":"1710.09829","code_links":[{"title":"labmlai/annotated_deep_learning_paper_implementations","url":"https://github.com/labmlai/annotated_deep_learning_paper_implementations"},{"title":"naturomics/CapsNet-Tensorflow","url":"https://github.com/naturomics/CapsNet-Tensorflow"},{"title":"Sarasra/models","url":"https://github.com/Sarasra/models"},{"title":"XifengGuo/CapsNet-Keras","url":"https://github.com/XifengGuo/CapsNet-Keras"},{"title":"gram-ai/capsule-networks","url":"https://github.com/gram-ai/capsule-networks"},{"title":"adambielski/capsnet-pytorch","url":"https://github.com/adambielski/capsnet-pytorch"},{"title":"naturomics/CapsLayer","url":"https://github.com/naturomics/CapsLayer"},{"title":"lalonderodney/SegCaps","url":"https://github.com/lalonderodney/SegCaps"},{"title":"EscVM/Efficient-CapsNet","url":"https://github.com/EscVM/Efficient-CapsNet"},{"title":"www0wwwjs1/Matrix-Capsules-EM-Tensorflow","url":"https://github.com/www0wwwjs1/Matrix-Capsules-EM-Tensorflow"},{"title":"soskek/dynamic_routing_between_capsules","url":"https://github.com/soskek/dynamic_routing_between_capsules"},{"title":"JunYeopLee/capsule-networks","url":"https://github.com/JunYeopLee/capsule-networks"},{"title":"glassroom/heinsen_routing","url":"https://github.com/glassroom/heinsen_routing"},{"title":"danielhavir/capsule-network","url":"https://github.com/danielhavir/capsule-network"},{"title":"anastasios-stamoulis/deep-learning-with-csharp-and-cntk","url":"https://github.com/anastasios-stamoulis/deep-learning-with-csharp-and-cntk"},{"title":"laubonghaudoi/CapsNet_guide_PyTorch","url":"https://github.com/laubonghaudoi/CapsNet_guide_PyTorch"},{"title":"Riroaki/CapsNet","url":"https://github.com/Riroaki/CapsNet"},{"title":"Cheng-Lin-Li/SegCaps","url":"https://github.com/Cheng-Lin-Li/SegCaps"},{"title":"hli2020/nn_encapsulation","url":"https://github.com/hli2020/nn_encapsulation"},{"title":"hli2020/nn_capsulation","url":"https://github.com/hli2020/nn_capsulation"},{"title":"yechengxi/LightCapsNet","url":"https://github.com/yechengxi/LightCapsNet"},{"title":"dragen1860/CapsNet-Pytorch","url":"https://github.com/dragen1860/CapsNet-Pytorch"},{"title":"ethanleet/CapsNet","url":"https://github.com/ethanleet/CapsNet"},{"title":"YushiChen/Conv-Caps-HSI-Classification","url":"https://github.com/YushiChen/Conv-Caps-HSI-Classification"},{"title":"Soonhwan-Kwon/capsnet.mxnet","url":"https://github.com/Soonhwan-Kwon/capsnet.mxnet"},{"title":"dedhiaparth98/capsule-network","url":"https://github.com/dedhiaparth98/capsule-network"},{"title":"laodar/tf_capsnet","url":"https://github.com/laodar/tf_capsnet"},{"title":"ameliajimenez/capsule-networks-medical-data-challenges","url":"https://github.com/ameliajimenez/capsule-networks-medical-data-challenges"},{"title":"chucooleg/capsnet_for_ner","url":"https://github.com/chucooleg/capsnet_for_ner"},{"title":"ecstayalive/Degenerate-capsule-neural-network","url":"https://github.com/ecstayalive/Degenerate-capsule-neural-network"},{"title":"sharathsarangmath/Vector-Capsule-Network-for-wild-animal-species-recognition-in-Camera-trap-images.","url":"https://github.com/sharathsarangmath/Vector-Capsule-Network-for-wild-animal-species-recognition-in-Camera-trap-images."},{"title":"sharathsarangmath/Vector-Capsule-Network-for-wild-animal-species-recognition-in-Camera-trap-images","url":"https://github.com/sharathsarangmath/Vector-Capsule-Network-for-wild-animal-species-recognition-in-Camera-trap-images"},{"title":"jelifysh/Capsule-Networks","url":"https://github.com/jelifysh/Capsule-Networks"},{"title":"Reatris/Capsule_Net","url":"https://github.com/Reatris/Capsule_Net"},{"title":"noureldinalaa/Capsule-Networks","url":"https://github.com/noureldinalaa/Capsule-Networks"},{"title":"JeremyCCHsu/CapsNet-tf","url":"https://github.com/JeremyCCHsu/CapsNet-tf"},{"title":"grassknoted/CDAE4InfoExtraction","url":"https://github.com/grassknoted/CDAE4InfoExtraction"},{"title":"lauromoraes/capsnet-promoter","url":"https://github.com/lauromoraes/capsnet-promoter"},{"title":"southworkscom/CapsNet-CNTK","url":"https://github.com/southworkscom/CapsNet-CNTK"},{"title":"pruthvip98/Image-Forgery-Detection","url":"https://github.com/pruthvip98/Image-Forgery-Detection"},{"title":"starsky68/capsnet_blk_prune","url":"https://github.com/starsky68/capsnet_blk_prune"},{"title":"akanimax/capsule-network-TensorFlow","url":"https://github.com/akanimax/capsule-network-TensorFlow"},{"title":"jihyeonseong/cardiocaps","url":"https://github.com/jihyeonseong/cardiocaps"},{"title":"dolaram/Deep-Reinforcement-Learning-using-Capsule-Network","url":"https://github.com/dolaram/Deep-Reinforcement-Learning-using-Capsule-Network"},{"title":"DanielLongo/CapsGAN","url":"https://github.com/DanielLongo/CapsGAN"},{"title":"JSerowik/Masters_Thesis","url":"https://github.com/JSerowik/Masters_Thesis"},{"title":"Suraj-Panwar/Capsule_Network_based_Deep_Q_learning","url":"https://github.com/Suraj-Panwar/Capsule_Network_based_Deep_Q_learning"},{"title":"jwzhanggy/isocapsnet","url":"https://github.com/jwzhanggy/isocapsnet"},{"title":"Ugenteraan/CapsNet-PyTorch","url":"https://github.com/Ugenteraan/CapsNet-PyTorch"},{"title":"leoniloris/CapsNet","url":"https://github.com/leoniloris/CapsNet"},{"title":"Utkarsh87/Capsule-Networks","url":"https://github.com/Utkarsh87/Capsule-Networks"},{"title":"Ugenteraan/CapsNet-MNIST-TF","url":"https://github.com/Ugenteraan/CapsNet-MNIST-TF"},{"title":"bhneo/Capsnet-Experiments","url":"https://github.com/bhneo/Capsnet-Experiments"},{"title":"bdsaglam/CapsNet","url":"https://github.com/bdsaglam/CapsNet"},{"title":"Oushesh/CapsClassifigner","url":"https://github.com/Oushesh/CapsClassifigner"},{"title":"mshaikh2/GANs_Comparison","url":"https://github.com/mshaikh2/GANs_Comparison"},{"title":"shashankmanjunath/capsnet","url":"https://github.com/shashankmanjunath/capsnet"},{"title":"OwenLeng/pytorch-capsule","url":"https://github.com/OwenLeng/pytorch-capsule"},{"title":"44825762/MyCapsNet","url":"https://github.com/44825762/MyCapsNet"},{"title":"dshelukh/CapsuleLearner","url":"https://github.com/dshelukh/CapsuleLearner"},{"title":"im-ant/Capsules-Guys","url":"https://github.com/im-ant/Capsules-Guys"},{"title":"dolaram/Capsule-Network-for-MNIST-classification","url":"https://github.com/dolaram/Capsule-Network-for-MNIST-classification"},{"title":"dudyu/capsnet","url":"https://github.com/dudyu/capsnet"},{"title":"sahil02235/CAPSULE-NETWORK-IMPLEMENTATION","url":"https://github.com/sahil02235/CAPSULE-NETWORK-IMPLEMENTATION"},{"title":"yikuide/pytorch-capsule","url":"https://github.com/yikuide/pytorch-capsule"},{"title":"jwwandy/DynamicRoutingBetweenCapsules-PyTorch","url":"https://github.com/jwwandy/DynamicRoutingBetweenCapsules-PyTorch"},{"title":"ecstayalive/capsule_nn","url":"https://github.com/ecstayalive/capsule_nn"},{"title":"Egesabanci/capsuleNetworks","url":"https://github.com/Egesabanci/capsuleNetworks"},{"title":"MS-Mind/MS-Code-07","url":"https://github.com/MS-Mind/MS-Code-07"},{"title":"razvanalex/CapsLayer","url":"https://github.com/razvanalex/CapsLayer"},{"title":"arnomoonens/capsule-networks","url":"https://github.com/arnomoonens/capsule-networks"},{"title":"DanielLongo/CapsNet-PyTorch","url":"https://github.com/DanielLongo/CapsNet-PyTorch"},{"title":"kazukiii/caps-net-pytorch","url":"https://github.com/kazukiii/caps-net-pytorch"},{"title":"Colonel-Hathi/CapsNet-Treemap-Perception-Trafficsign","url":"https://github.com/Colonel-Hathi/CapsNet-Treemap-Perception-Trafficsign"},{"title":"arsenzaryan/CapsNet","url":"https://github.com/arsenzaryan/CapsNet"},{"title":"leekh7411/Capsule-Network-MNIST-Visualization","url":"https://github.com/leekh7411/Capsule-Network-MNIST-Visualization"},{"title":"Ian-Liao/SegCaps","url":"https://github.com/Ian-Liao/SegCaps"}],"syntology":{"n":118,"n_ran":18,"n_unverified":100,"n_pointer_only":12}}},{"leaderboard":"/sota/image-classification-on-nct-crc-he-100k","slug":"image-classification-on-nct-crc-he-100k","dataset":"NCT-CRC-HE-100K","dataset_url":"/dataset/nct-crc-he-100k","rows_in_archive":1,"metrics":["F1"],"first_row_in_archive_order":{"model":"SAG-ViT","paper_title":"SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers","paper_url":"/paper/sag-vit-a-scale-aware-high-fidelity-patching","paper_date":"2024-11-14","arxiv_id":"2411.09420","code_links":[{"title":"shravan-18/SAG-ViT","url":"https://github.com/shravan-18/SAG-ViT"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-no-background-rgb","slug":"image-classification-on-no-background-rgb","dataset":"No Background RGB Arabic Alphabets Sign Language Dataset","dataset_url":"/dataset/no-background-rgb-arabic-alphabets-sign","rows_in_archive":1,"metrics":["Validation Accuracy"],"first_row_in_archive_order":{"model":"ArabSignNet","paper_title":"Deep Learning Recognition for Arabic Alphabet Sign Language RGB Dataset","paper_url":"/paper/deep-learning-recognition-for-arabic-alphabet","paper_date":"2024-03-11","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-pascal-voc-2007","slug":"image-classification-on-pascal-voc-2007","dataset":"PASCAL VOC 2007","dataset_url":"/dataset/pascal-voc-2007","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"NNCLR","paper_title":"With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations","paper_url":"/paper/with-a-little-help-from-my-friends-nearest","paper_date":"2021-04-29","arxiv_id":"2104.14548","code_links":[{"title":"lightly-ai/lightly","url":"https://github.com/lightly-ai/lightly"},{"title":"keras-team/keras-io","url":"https://github.com/keras-team/keras-io/blob/master/examples/vision/nnclr.py"},{"title":"vturrisi/solo-learn","url":"https://github.com/vturrisi/solo-learn"},{"title":"beresandras/contrastive-classification-keras","url":"https://github.com/beresandras/contrastive-classification-keras"}],"syntology":{"n":5,"n_ran":4,"n_unverified":1,"n_pointer_only":0}}},{"leaderboard":"/sota/image-classification-on-pets-sam","slug":"image-classification-on-pets-sam","dataset":"Pets SAM","dataset_url":null,"rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"efficient adaptive ensembling","paper_title":"Efficient Adaptive Ensembling for Image Classification","paper_url":"/paper/efficient-adaptive-ensembling-for-image","paper_date":"2022-06-15","arxiv_id":"2206.07394","code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-prima","slug":"image-classification-on-prima","dataset":"PRImA","dataset_url":"/dataset/prima","rows_in_archive":1,"metrics":["Percentage correct"],"first_row_in_archive_order":{"model":"ResNet-152 2x (RS training)","paper_title":"Revisiting ResNets: Improved Training and Scaling Strategies","paper_url":"/paper/revisiting-resnets-improved-training-and","paper_date":"2021-03-13","arxiv_id":"2103.07579","code_links":[{"title":"rwightman/pytorch-image-models","url":"https://github.com/rwightman/pytorch-image-models"},{"title":"tensorflow/tpu","url":"https://github.com/tensorflow/tpu/tree/master/models/official/resnet/resnet_rs"},{"title":"nachiket273/pytorch_resnet_rs","url":"https://github.com/nachiket273/pytorch_resnet_rs"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-qmnist","slug":"image-classification-on-qmnist","dataset":"QMNIST","dataset_url":"/dataset/qmnist","rows_in_archive":1,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"Deep regularization","paper_title":"Deep regularization and direct training of the inner layers of Neural Networks with Kernel Flows","paper_url":"/paper/deep-regularization-and-direct-training-of","paper_date":"2020-02-19","arxiv_id":"2002.08335","code_links":[{"title":"kernel-enthusiasts/KF_NN2","url":"https://github.com/kernel-enthusiasts/KF_NN2"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-rgb-arabic-alphabet","slug":"image-classification-on-rgb-arabic-alphabet","dataset":"RGB Arabic Alphabet Sign Language (AASL) dataset","dataset_url":"/dataset/rgb-arabic-alphabet-sign-language-aasl","rows_in_archive":1,"metrics":["Validation Accuracy"],"first_row_in_archive_order":{"model":"ArabSignNet","paper_title":"Deep Learning Recognition for Arabic Alphabet Sign Language RGB Dataset","paper_url":"/paper/deep-learning-recognition-for-arabic-alphabet","paper_date":"2024-03-11","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/image-classification-on-sars-cov-2","slug":"image-classification-on-sars-cov-2","dataset":"SARS-COV-2","dataset_url":null,"rows_in_archive":1,"metrics":["1:1 Accuracy"],"first_row_in_archive_order":{"model":"Fuzzy rank-based fusion of CNN models using Gompertz function","paper_title":"Fuzzy Rank-based Fusion of CNN Models using Gompertz Function for Screening COVID-19 CT-Scans","paper_url":"/paper/fuzzy-rank-based-fusion-of-cnn-models-using","paper_date":"2021-07-08","arxiv_id":null,"code_links":[{"title":"Rohit-Kundu/COVID-Detection-Gompertz-Function-Ensemble","url":"https://github.com/Rohit-Kundu/COVID-Detection-Gompertz-Function-Ensemble"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-so2sat-lcz42","slug":"image-classification-on-so2sat-lcz42","dataset":"So2Sat LCZ42","dataset_url":"/dataset/so2sat-lcz42","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"ResNet50","paper_title":"In-domain representation learning for remote sensing","paper_url":"/paper/in-domain-representation-learning-for-remote-1","paper_date":"2019-11-15","arxiv_id":"1911.06721","code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research/tree/master/remote_sensing_representations"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-split-cifar-10","slug":"image-classification-on-split-cifar-10","dataset":"Split CIFAR-10","dataset_url":null,"rows_in_archive":1,"metrics":["Percentage Average accuracy - 5 tasks"],"first_row_in_archive_order":{"model":"Model with negotiation paradigm","paper_title":"Negotiated Representations to Prevent Forgetting in Machine Learning Applications","paper_url":"/paper/negotiated-representations-to-prevent","paper_date":"2023-11-30","arxiv_id":"2312.00237","code_links":[{"title":"nurikorhan/negotiated-representations-for-continual-learning","url":"https://github.com/nurikorhan/negotiated-representations-for-continual-learning"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-split-fashion-m-nist","slug":"image-classification-on-split-fashion-m-nist","dataset":"Split Fashion M-NIST","dataset_url":null,"rows_in_archive":1,"metrics":["Percentage Average accuracy - 5 tasks"],"first_row_in_archive_order":{"model":"Model with negotiation paradigm","paper_title":"Negotiated Representations to Prevent Forgetting in Machine Learning Applications","paper_url":"/paper/negotiated-representations-to-prevent","paper_date":"2023-11-30","arxiv_id":"2312.00237","code_links":[{"title":"nurikorhan/negotiated-representations-for-continual-learning","url":"https://github.com/nurikorhan/negotiated-representations-for-continual-learning"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-split-m-nist","slug":"image-classification-on-split-m-nist","dataset":"Split M-NIST","dataset_url":null,"rows_in_archive":1,"metrics":["Percentage Average accuracy - 5 tasks"],"first_row_in_archive_order":{"model":"Model with negotiation paradigm","paper_title":"Negotiated Representations to Prevent Forgetting in Machine Learning Applications","paper_url":"/paper/negotiated-representations-to-prevent","paper_date":"2023-11-30","arxiv_id":"2312.00237","code_links":[{"title":"nurikorhan/negotiated-representations-for-continual-learning","url":"https://github.com/nurikorhan/negotiated-representations-for-continual-learning"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-sports10","slug":"image-classification-on-sports10","dataset":"Sports10","dataset_url":"/dataset/sports10","rows_in_archive":1,"metrics":["Validation Accuracy"],"first_row_in_archive_order":{"model":"Max Margin Contrastive","paper_title":"Contrastive Learning of Generalized Game Representations","paper_url":"/paper/contrastive-learning-of-generalized-game","paper_date":"2021-06-18","arxiv_id":"2106.10060","code_links":[{"title":"ChintanTrivedi/contrastive-game-representations","url":"https://github.com/ChintanTrivedi/contrastive-game-representations"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-stanford-online","slug":"image-classification-on-stanford-online","dataset":"Stanford Online Products","dataset_url":"/dataset/stanford-online-products","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"µ2Net+ (ViT-L/16)","paper_title":"A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems","paper_url":"/paper/a-continual-development-methodology-for-large","paper_date":"2022-09-15","arxiv_id":"2209.07326","code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research/tree/master/muNet"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-sun397","slug":"image-classification-on-sun397","dataset":"SUN397","dataset_url":"/dataset/sun397","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"TransBoost-ResNet50","paper_title":"TransBoost: Improving the Best ImageNet Performance using Deep Transduction","paper_url":"/paper/transboost-improving-the-best-imagenet","paper_date":"2022-05-26","arxiv_id":"2205.13331","code_links":[{"title":"omerb01/transboost","url":"https://github.com/omerb01/transboost"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-surrey-asl","slug":"image-classification-on-surrey-asl","dataset":"Surrey ASL","dataset_url":null,"rows_in_archive":1,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"E2E-3M","paper_title":"Rethinking Recurrent Neural Networks and Other Improvements for Image Classification","paper_url":"/paper/rethinking-recurrent-neural-networks-and","paper_date":"2020-07-30","arxiv_id":"2007.15161","code_links":[{"title":"leonlha/e2e-3m","url":"https://github.com/leonlha/e2e-3m"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-training-and","slug":"image-classification-on-training-and","dataset":"Training and validation dataset of capsule vision 2024 challenge.","dataset_url":null,"rows_in_archive":1,"metrics":["Total Accuracy"],"first_row_in_archive_order":{"model":"BiomedCLIP+PubmedBERT","paper_title":"A Multimodal Approach For Endoscopic VCE Image Classification Using BiomedCLIP-PubMedBERT","paper_url":"/paper/a-multimodal-approach-for-endoscopic-vce","paper_date":"2024-10-25","arxiv_id":"2410.19944","code_links":[{"title":"Satyajithchary/MedInfoLab_Capsule_Vision_2024_Challenge","url":"https://github.com/Satyajithchary/MedInfoLab_Capsule_Vision_2024_Challenge"}],"syntology":null}},{"leaderboard":"/sota/image-classification-on-vizwiz-classification","slug":"image-classification-on-vizwiz-classification","dataset":"VizWiz-Classification","dataset_url":"/dataset/vizwiz-classification","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"VOLO-D5","paper_title":"VOLO: Vision Outlooker for Visual Recognition","paper_url":"/paper/volo-vision-outlooker-for-visual-recognition","paper_date":"2021-06-24","arxiv_id":"2106.13112","code_links":[{"title":"rwightman/pytorch-image-models","url":"https://github.com/rwightman/pytorch-image-models"},{"title":"xmu-xiaoma666/External-Attention-pytorch","url":"https://github.com/xmu-xiaoma666/External-Attention-pytorch/blob/master/attention/OutlookAttention.py"},{"title":"BR-IDL/PaddleViT","url":"https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/VOLO"},{"title":"sail-sg/volo","url":"https://github.com/sail-sg/volo"},{"title":"leondgarse/keras_cv_attention_models","url":"https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/volo"},{"title":"mindspore-courses/External-Attention-MindSpore","url":"https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/OutlookAttention.py"},{"title":"mindspore-courses/External-Attention-MindSpore","url":"https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/VOLO.py"}],"syntology":{"n":6,"n_ran":1,"n_unverified":5,"n_pointer_only":0}}},{"leaderboard":null,"slug":"image-classification-on-chbh7051-vit-base","dataset":"chbh7051/vit-base-driver-drowsiness-detection","dataset_url":null,"rows_in_archive":0,"metrics":["Accuracy"],"first_row_in_archive_order":null},{"leaderboard":null,"slug":"image-classification-on-custom-dataset","dataset":"Custom Dataset","dataset_url":null,"rows_in_archive":0,"metrics":["Accuracy"],"first_row_in_archive_order":null},{"leaderboard":null,"slug":"image-classification-on-custom-deepfake","dataset":"Custom DeepFake Dataset","dataset_url":null,"rows_in_archive":0,"metrics":["value"],"first_row_in_archive_order":null},{"leaderboard":null,"slug":"image-classification-on-finetuned-websites","dataset":"finetuned-websites","dataset_url":null,"rows_in_archive":0,"metrics":["Accuracy"],"first_row_in_archive_order":null},{"leaderboard":null,"slug":"image-classification-on-human-action","dataset":"Human_Action_Recognition","dataset_url":null,"rows_in_archive":0,"metrics":["Accuracy"],"first_row_in_archive_order":null},{"leaderboard":null,"slug":"image-classification-on-image-folder","dataset":"image_folder","dataset_url":null,"rows_in_archive":0,"metrics":["F1"],"first_row_in_archive_order":null},{"leaderboard":null,"slug":"image-classification-on-imagefolder","dataset":"imagefolder","dataset_url":null,"rows_in_archive":0,"metrics":["Accuracy","F1"],"first_row_in_archive_order":null},{"leaderboard":null,"slug":"image-classification-on-indian-food-images","dataset":"indian_food_images","dataset_url":null,"rows_in_archive":0,"metrics":["Accuracy"],"first_row_in_archive_order":null},{"leaderboard":null,"slug":"image-classification-on-mridataset","dataset":"mriDataSet","dataset_url":null,"rows_in_archive":0,"metrics":["Accuracy"],"first_row_in_archive_order":null},{"leaderboard":null,"slug":"image-classification-on-new-plant-diseases","dataset":"New Plant Diseases Dataset","dataset_url":"/dataset/new-plant-diseases-dataset","rows_in_archive":0,"metrics":["Accuracy"],"first_row_in_archive_order":null},{"leaderboard":null,"slug":"image-classification-on-pcam","dataset":"PCam","dataset_url":"/dataset/pcam","rows_in_archive":0,"metrics":["Accuracy"],"first_row_in_archive_order":null}],"datasets":[{"url":"/dataset/cifar-10","name":"CIFAR-10","full_name":"CIFAR-10","num_papers_in_archive":16145},{"url":"/dataset/imagenet","name":"ImageNet","full_name":"","num_papers_in_archive":15430},{"url":"/dataset/cifar-100","name":"CIFAR-100","full_name":"","num_papers_in_archive":9045},{"url":"/dataset/mnist","name":"MNIST","full_name":"","num_papers_in_archive":7651},{"url":"/dataset/celeba","name":"CelebA","full_name":"CelebFaces Attributes Dataset","num_papers_in_archive":3477},{"url":"/dataset/svhn","name":"SVHN","full_name":"Street View House Numbers","num_papers_in_archive":3406},{"url":"/dataset/fashion-mnist","name":"Fashion-MNIST","full_name":"","num_papers_in_archive":3202},{"url":"/dataset/cub-200-2011","name":"CUB-200-2011","full_name":"Caltech-UCSD Birds-200-2011","num_papers_in_archive":2235},{"url":"/dataset/oxford-102-flower","name":"Oxford 102 Flower","full_name":"102 Category Flower Dataset","num_papers_in_archive":1307},{"url":"/dataset/tiny-imagenet","name":"Tiny ImageNet","full_name":"Tiny ImageNet","num_papers_in_archive":1232},{"url":"/dataset/stl-10","name":"STL-10","full_name":"Self-Taught Learning 10","num_papers_in_archive":1092},{"url":"/dataset/dtd","name":"DTD","full_name":"Describable Textures Dataset","num_papers_in_archive":870},{"url":"/dataset/food-101","name":"Food-101","full_name":"","num_papers_in_archive":805},{"url":"/dataset/stanford-cars","name":"Stanford Cars","full_name":"","num_papers_in_archive":790},{"url":"/dataset/eurosat","name":"EuroSAT","full_name":"EuroSAT","num_papers_in_archive":687},{"url":"/dataset/inaturalist","name":"iNaturalist","full_name":"","num_papers_in_archive":603},{"url":"/dataset/places205","name":"Places205","full_name":"","num_papers_in_archive":525},{"url":"/dataset/fgvc-aircraft-1","name":"FGVC-Aircraft","full_name":"","num_papers_in_archive":520},{"url":"/dataset/bdd100k","name":"BDD100K","full_name":"","num_papers_in_archive":469},{"url":"/dataset/caltech-256","name":"Caltech-256","full_name":"","num_papers_in_archive":401},{"url":"/dataset/esc-50","name":"ESC-50","full_name":"ESC-50","num_papers_in_archive":387},{"url":"/dataset/gtsrb","name":"GTSRB","full_name":"German Traffic Sign Recognition Benchmark","num_papers_in_archive":374},{"url":"/dataset/tieredimagenet","name":"tieredImageNet","full_name":"tieredImageNet","num_papers_in_archive":317},{"url":"/dataset/clothing1m","name":"Clothing1M","full_name":"","num_papers_in_archive":288},{"url":"/dataset/imagenet-sketch","name":"ImageNet-Sketch","full_name":"","num_papers_in_archive":268},{"url":"/dataset/emnist","name":"EMNIST","full_name":"Extended MNIST","num_papers_in_archive":264},{"url":"/dataset/nas-bench-201","name":"NAS-Bench-201","full_name":"","num_papers_in_archive":260},{"url":"/dataset/yfcc100m","name":"YFCC100M","full_name":"","num_papers_in_archive":243},{"url":"/dataset/stanford-online-products","name":"Stanford Online Products","full_name":"Stanford Online Products","num_papers_in_archive":231},{"url":"/dataset/vgg-sound","name":"VGG-Sound","full_name":"","num_papers_in_archive":211},{"url":"/dataset/ai2d","name":"AI2D","full_name":"AI2 Diagrams","num_papers_in_archive":207},{"url":"/dataset/vtab","name":"VTAB","full_name":"Visual Task Adaptation Benchmark","num_papers_in_archive":202},{"url":"/dataset/cinic-10","name":"CINIC-10","full_name":"CINIC-10","num_papers_in_archive":197},{"url":"/dataset/resisc45","name":"RESISC45","full_name":"RESISC45","num_papers_in_archive":187},{"url":"/dataset/extended-yale-b-1","name":"Extended Yale B","full_name":"","num_papers_in_archive":185},{"url":"/dataset/webvision-database","name":"WebVision","full_name":"","num_papers_in_archive":179},{"url":"/dataset/labelme","name":"LabelMe","full_name":"","num_papers_in_archive":178},{"url":"/dataset/objectnet","name":"ObjectNet","full_name":"","num_papers_in_archive":155},{"url":"/dataset/fmow","name":"fMoW","full_name":"Functional Map of the World","num_papers_in_archive":144},{"url":"/dataset/oxford5k","name":"Oxford5k","full_name":"Oxford Buildings","num_papers_in_archive":137},{"url":"/dataset/meta-dataset","name":"Meta-Dataset","full_name":"","num_papers_in_archive":128},{"url":"/dataset/pascal-voc-2007","name":"PASCAL VOC 2007","full_name":"PASCAL VOC 2007","num_papers_in_archive":126},{"url":"/dataset/jft-300m","name":"JFT-300M","full_name":"JFT-300M","num_papers_in_archive":123},{"url":"/dataset/kvasir","name":"Kvasir","full_name":"The Kvasir Dataset","num_papers_in_archive":117},{"url":"/dataset/imagenet-32","name":"ImageNet-32","full_name":"","num_papers_in_archive":112},{"url":"/dataset/smallnorb","name":"smallNORB","full_name":"","num_papers_in_archive":112},{"url":"/dataset/n-caltech-101","name":"N-Caltech 101","full_name":"Neuromorphic-Caltech101","num_papers_in_archive":110},{"url":"/dataset/pcam","name":"PCam","full_name":"PatchCamelyon","num_papers_in_archive":110},{"url":"/dataset/stylized-imagenet","name":"Stylized ImageNet","full_name":"Stylized ImageNet","num_papers_in_archive":106},{"url":"/dataset/dvs128-gesture-dataset","name":"DVS128 Gesture","full_name":"","num_papers_in_archive":103},{"url":"/dataset/tiny-images","name":"Tiny Images","full_name":"","num_papers_in_archive":103},{"url":"/dataset/cifar-10n","name":"CIFAR-10N","full_name":"Real-World Human Annotations","num_papers_in_archive":97},{"url":"/dataset/kuzushiji-mnist","name":"Kuzushiji-MNIST","full_name":"","num_papers_in_archive":97},{"url":"/dataset/oxford-iiit-pets","name":"Oxford-IIIT Pet Dataset","full_name":"Oxford-IIIT Pet Dataset","num_papers_in_archive":90},{"url":"/dataset/imagenet-o","name":"ImageNet-O","full_name":"","num_papers_in_archive":89},{"url":"/dataset/abstractreasoning","name":"AbstractReasoning","full_name":"AbstractReasoning","num_papers_in_archive":86},{"url":"/dataset/bigearthnet","name":"BigEarthNet","full_name":"","num_papers_in_archive":85},{"url":"/dataset/chestx-ray8","name":"ChestX-ray8","full_name":"ChestX-ray8","num_papers_in_archive":81},{"url":"/dataset/plantvillage","name":"PlantVillage","full_name":"","num_papers_in_archive":68},{"url":"/dataset/cifar-100n","name":"CIFAR-100N","full_name":"Real-World Human Annotations","num_papers_in_archive":66},{"url":"/dataset/places365","name":"Places365","full_name":"","num_papers_in_archive":65},{"url":"/dataset/oxford-iiit-pets-1","name":"Oxford-IIIT Pets","full_name":"","num_papers_in_archive":59},{"url":"/dataset/web-of-science-dataset","name":"WOS","full_name":"Web of Science Dataset","num_papers_in_archive":59},{"url":"/dataset/pgm","name":"PGM","full_name":"Procedurally Generated Matrices (PGM)","num_papers_in_archive":57},{"url":"/dataset/minc","name":"MINC","full_name":"Materials in Context Database","num_papers_in_archive":56},{"url":"/dataset/tiny-imagenet-c","name":"Tiny-ImageNet-C","full_name":"","num_papers_in_archive":55},{"url":"/dataset/multimnist","name":"MultiMNIST","full_name":"","num_papers_in_archive":52},{"url":"/dataset/sun397","name":"SUN397","full_name":"SUN397","num_papers_in_archive":52},{"url":"/dataset/cars196","name":"CARS196","full_name":"","num_papers_in_archive":43},{"url":"/dataset/jft-3b","name":"JFT-3B","full_name":"JFT-3B","num_papers_in_archive":41},{"url":"/dataset/million-aid","name":"Million-AID","full_name":"","num_papers_in_archive":41},{"url":"/dataset/visual-wake-words","name":"Visual Wake Words","full_name":"","num_papers_in_archive":40},{"url":"/dataset/imagenette","name":"Imagenette","full_name":"Imagenette","num_papers_in_archive":38},{"url":"/dataset/sun-attribute","name":"SUN Attribute","full_name":"SUN Attribute","num_papers_in_archive":38},{"url":"/dataset/open-images-v4","name":"Open Images V4","full_name":"","num_papers_in_archive":37},{"url":"/dataset/imagenet-64","name":"ImageNet-64","full_name":"","num_papers_in_archive":35},{"url":"/dataset/imagenet-p","name":"ImageNet-P","full_name":"","num_papers_in_archive":32},{"url":"/dataset/omnibenchmark","name":"OmniBenchmark","full_name":"","num_papers_in_archive":29},{"url":"/dataset/eyeq","name":"EyeQ","full_name":"","num_papers_in_archive":28},{"url":"/dataset/inat2021","name":"iNat2021","full_name":"iNaturalist 2021","num_papers_in_archive":27},{"url":"/dataset/flickrlogos-32","name":"FlickrLogos-32","full_name":"","num_papers_in_archive":26},{"url":"/dataset/qmnist","name":"QMNIST","full_name":null,"num_papers_in_archive":26},{"url":"/dataset/urbancars","name":"UrbanCars","full_name":"","num_papers_in_archive":26},{"url":"/dataset/bam","name":"BAM!","full_name":"Behance Artistic Media","num_papers_in_archive":25},{"url":"/dataset/elevater","name":"ELEVATER","full_name":"Evaluation of Language-augmented Visual Task-level Transfer","num_papers_in_archive":25},{"url":"/dataset/imagenet-w","name":"ImageNet-W","full_name":"ImageNet-Watermark","num_papers_in_archive":23},{"url":"/dataset/ip102","name":"IP102","full_name":"","num_papers_in_archive":22},{"url":"/dataset/vizwiz-classification","name":"VizWiz-Classification","full_name":"","num_papers_in_archive":22},{"url":"/dataset/lag","name":"LAG","full_name":"Large-scale Attention based Glaucoma","num_papers_in_archive":21},{"url":"/dataset/mseg","name":"MSeg","full_name":"","num_papers_in_archive":21},{"url":"/dataset/ethos","name":"ETHOS","full_name":"multi-labEl haTe speecH detectiOn dataSet","num_papers_in_archive":20},{"url":"/dataset/plantdoc","name":"PlantDoc","full_name":"","num_papers_in_archive":20},{"url":"/dataset/chaoyang","name":"Chaoyang","full_name":"","num_papers_in_archive":19},{"url":"/dataset/deepfish","name":"DeepFish","full_name":"","num_papers_in_archive":19},{"url":"/dataset/lamem","name":"LaMem","full_name":"","num_papers_in_archive":18},{"url":"/dataset/mlrsnet","name":"MLRSNet","full_name":"","num_papers_in_archive":17},{"url":"/dataset/femnist","name":"FEMNIST","full_name":"Federated Extended MNIST","num_papers_in_archive":16},{"url":"/dataset/food2k","name":"Food2K","full_name":"","num_papers_in_archive":14},{"url":"/dataset/n-mnist","name":"N-MNIST","full_name":"Neuromorphic-MNIST","num_papers_in_archive":14},{"url":"/dataset/tau-urban-acoustic-scenes-2019","name":"TAU Urban Acoustic Scenes 2019","full_name":"TAU Urban Acoustic Scenes 2019","num_papers_in_archive":14},{"url":"/dataset/cats-vs-dogs","name":"Cats and Dogs","full_name":"","num_papers_in_archive":13},{"url":"/dataset/causal3dident","name":"Causal3DIdent","full_name":"","num_papers_in_archive":12},{"url":"/dataset/hyper-kvasir-dataset","name":"Hyper-Kvasir Dataset","full_name":"","num_papers_in_archive":12},{"url":"/dataset/imagenet-100","name":"ImageNet-100 (TEMI Split)","full_name":"ImageNet-100 (TEMI Split)","num_papers_in_archive":12},{"url":"/dataset/bcn-20000","name":"BCN_20000","full_name":"BCN_20000","num_papers_in_archive":11},{"url":"/dataset/bcdalnmp","name":"BCNB","full_name":"Early Breast Cancer Core-Needle Biopsy WSI","num_papers_in_archive":11},{"url":"/dataset/breakhis","name":"BreakHis","full_name":"Breast Cancer Histopathological Database","num_papers_in_archive":11},{"url":"/dataset/foodx-251","name":"FoodX-251","full_name":"","num_papers_in_archive":11},{"url":"/dataset/gashissdb","name":"GasHisSDB","full_name":"","num_papers_in_archive":11},{"url":"/dataset/so2sat-lcz42","name":"So2Sat LCZ42","full_name":"","num_papers_in_archive":11},{"url":"/dataset/kuzushiji-49","name":"Kuzushiji-49","full_name":"","num_papers_in_archive":10},{"url":"/dataset/amstertime","name":"AmsterTime","full_name":"AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain Shift","num_papers_in_archive":9},{"url":"/dataset/icartoonface","name":"iCartoonFace","full_name":null,"num_papers_in_archive":9},{"url":"/dataset/iwildcam2020-wilds","name":"iWildCam2020-WILDS","full_name":"","num_papers_in_archive":9},{"url":"/dataset/kmnist","name":"KMNIST","full_name":"","num_papers_in_archive":9},{"url":"/dataset/aider","name":"AIDER","full_name":"","num_papers_in_archive":8},{"url":"/dataset/coloninst-v1","name":"ColonINST-v1","full_name":"","num_papers_in_archive":8},{"url":"/dataset/coloninst-v1-seen","name":"ColonINST-v1 (Seen)","full_name":"","num_papers_in_archive":8},{"url":"/dataset/coloninst-v1-uneen","name":"ColonINST-v1 (Unseen)","full_name":"","num_papers_in_archive":8},{"url":"/dataset/nct-crc-he-100k","name":"NCT-CRC-HE-100K","full_name":"","num_papers_in_archive":8},{"url":"/dataset/artbench-10","name":"ArtBench-10 (32x32)","full_name":"","num_papers_in_archive":7},{"url":"/dataset/bamboo","name":"Bamboo","full_name":"","num_papers_in_archive":7},{"url":"/dataset/book-cover-dataset","name":"Book Cover Dataset","full_name":"","num_papers_in_archive":7},{"url":"/dataset/df20","name":"DF20","full_name":"Danish Fungi 2020","num_papers_in_archive":7},{"url":"/dataset/dfuc2021","name":"DFUC2021","full_name":"Diabetic Foot Ulcers 2021","num_papers_in_archive":7},{"url":"/dataset/grocery-store","name":"Grocery Store","full_name":null,"num_papers_in_archive":7},{"url":"/dataset/imagenet-9","name":"ImageNet-9","full_name":"","num_papers_in_archive":7},{"url":"/dataset/kannada-mnist","name":"Kannada-MNIST","full_name":"","num_papers_in_archive":7},{"url":"/dataset/kaokore","name":"KaoKore","full_name":"","num_papers_in_archive":7},{"url":"/dataset/numtadb","name":"NumtaDB","full_name":"Assembled Bengali Handwritten Digits","num_papers_in_archive":7},{"url":"/dataset/pmdata","name":"PMData","full_name":"","num_papers_in_archive":7},{"url":"/dataset/ps-battles","name":"PS-Battles","full_name":"","num_papers_in_archive":7},{"url":"/dataset/si-score","name":"SI-SCORE","full_name":"Synthetic Interventions on Scenes for Robustness Evaluation","num_papers_in_archive":7},{"url":"/dataset/colored-mnist-spurious-correlation","name":"Colored-MNIST(with spurious correlation)","full_name":"","num_papers_in_archive":6},{"url":"/dataset/f-celeba-10-tasks","name":"F-CelebA (10 tasks)","full_name":"Federated-CelebA (10 tasks)","num_papers_in_archive":6},{"url":"/dataset/food-101n","name":"Food-101N","full_name":"Food-101N","num_papers_in_archive":6},{"url":"/dataset/galaxy-zoo-decals","name":"Galaxy Zoo DECaLS","full_name":"","num_papers_in_archive":6},{"url":"/dataset/malaria-dataset","name":"Malaria Dataset","full_name":"","num_papers_in_archive":6},{"url":"/dataset/ood-cv","name":"OOD-CV","full_name":"Out Of Distribution Generalization in Computer Vision","num_papers_in_archive":6},{"url":"/dataset/open-images-v7","name":"Open Images V7","full_name":"","num_papers_in_archive":6},{"url":"/dataset/red-miniimagenet-20-label-noise","name":"Red MiniImageNet 20% label noise","full_name":"","num_papers_in_archive":6},{"url":"/dataset/red-miniimagenet-40-label-noise","name":"Red MiniImageNet 40% label noise","full_name":"","num_papers_in_archive":6},{"url":"/dataset/red-miniimagenet-80-label-noise","name":"Red MiniImageNet 80% label noise","full_name":"","num_papers_in_archive":6},{"url":"/dataset/urban-environments-dataset","name":"Urban Environments","full_name":"","num_papers_in_archive":6},{"url":"/dataset/herlev","name":"HErlev","full_name":"HErlev Pap Smear Dataset","num_papers_in_archive":5},{"url":"/dataset/imagenet-hard","name":"ImageNet-Hard","full_name":"","num_papers_in_archive":5},{"url":"/dataset/imagenet-patch","name":"ImageNet-Patch","full_name":"","num_papers_in_archive":5},{"url":"/dataset/insplad","name":"InsPLAD","full_name":"Inspection Power Line Asset Dataset","num_papers_in_archive":5},{"url":"/dataset/intel-image-classification","name":"Intel Image Classification","full_name":"","num_papers_in_archive":5},{"url":"/dataset/mumin","name":"MuMiN","full_name":"","num_papers_in_archive":5},{"url":"/dataset/nih-cxr-lt","name":"NIH-CXR-LT","full_name":"Long-tailed (LT) NIH ChestXRay14","num_papers_in_archive":5},{"url":"/dataset/rf100","name":"RF100","full_name":"Roboflow 100","num_papers_in_archive":5},{"url":"/dataset/sewer-ml","name":"Sewer-ML","full_name":"","num_papers_in_archive":5},{"url":"/dataset/sipakmed","name":"SIPaKMeD","full_name":"SIPaKMeD Pap Smear dataset","num_papers_in_archive":5},{"url":"/dataset/sports10","name":"Sports10","full_name":"","num_papers_in_archive":5},{"url":"/dataset/tencent-ml-images","name":"Tencent ML-Images","full_name":"","num_papers_in_archive":5},{"url":"/dataset/ci-mnist","name":"CI-MNIST","full_name":"Correlated and Imbalanced MNIST","num_papers_in_archive":4},{"url":"/dataset/diagset","name":"DiagSet","full_name":"","num_papers_in_archive":4},{"url":"/dataset/ethec","name":"ETHEC","full_name":"ETH Entomological Collection (ETHEC) Dataset","num_papers_in_archive":4},{"url":"/dataset/eurosat-sar","name":"EuroSAT-SAR","full_name":"","num_papers_in_archive":4},{"url":"/dataset/kth-tips2","name":"KTH-TIPS2","full_name":"","num_papers_in_archive":4},{"url":"/dataset/kuzushiji-kanji","name":"Kuzushiji-Kanji","full_name":"","num_papers_in_archive":4},{"url":"/dataset/limuc","name":"LIMUC","full_name":"Labeled Images for Ulcerative Colitis","num_papers_in_archive":4},{"url":"/dataset/mimic-cxr-lt","name":"MIMIC-CXR-LT","full_name":"long-tailed version of MIMIC-CXR","num_papers_in_archive":4},{"url":"/dataset/mnist-large-scale-dataset","name":"MNIST Large Scale dataset","full_name":"","num_papers_in_archive":4},{"url":"/dataset/oracle-mnist","name":"Oracle-MNIST","full_name":"Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms","num_papers_in_archive":4},{"url":"/dataset/whu-hi","name":"WHU-Hi","full_name":"Wuhan UAV-borne hyperspectral image","num_papers_in_archive":4},{"url":"/dataset/acl-fig","name":"ACL-Fig","full_name":"","num_papers_in_archive":3},{"url":"/dataset/animals-10","name":"Animals-10","full_name":"","num_papers_in_archive":3},{"url":"/dataset/atlas","name":"Atlas","full_name":"","num_papers_in_archive":3},{"url":"/dataset/deic-benchmark","name":"DEIC Benchmark","full_name":"Data-Efficient Image Classification Benchmark","num_papers_in_archive":3},{"url":"/dataset/diffusion-deepfake","name":"Diffusion Deepfake","full_name":"","num_papers_in_archive":3},{"url":"/dataset/dirty-mnist","name":"Dirty-MNIST","full_name":"","num_papers_in_archive":3},{"url":"/dataset/firerisk","name":"FireRisk","full_name":"FireRisk: A Remote Sensing Dataset for Fire Risk Assessment","num_papers_in_archive":3},{"url":"/dataset/fmd-texture","name":"FMD (materials)","full_name":"Flickr Material Dataset","num_papers_in_archive":3},{"url":"/dataset/image-and-video-advertisements","name":"Image and Video Advertisements","full_name":"","num_papers_in_archive":3},{"url":"/dataset/lks","name":"LKS","full_name":"Liver Kidney Stomach","num_papers_in_archive":3},{"url":"/dataset/lsa16","name":"LSA16","full_name":"Lengua de Señas Argentina - 16 Handshapes classes","num_papers_in_archive":3},{"url":"/dataset/ofdiw","name":"OFDIW","full_name":"OnFocus Detection In the Wild","num_papers_in_archive":3},{"url":"/dataset/rgb-arabic-alphabets-sign-language-dataset","name":"RGB Arabic Alphabets Sign Language Dataset","full_name":"","num_papers_in_archive":3},{"url":"/dataset/sidd-network","name":"SIDD-Image","full_name":"Segmented Intrusion Detection Dataset","num_papers_in_archive":3},{"url":"/dataset/stream-51","name":"Stream-51","full_name":"","num_papers_in_archive":3},{"url":"/dataset/advnet-1","name":"AdvNet","full_name":"","num_papers_in_archive":2},{"url":"/dataset/asirra","name":"ASIRRA","full_name":"(Animal Species Image Recognition for Restricting Access","num_papers_in_archive":2},{"url":"/dataset/carben","name":"CARBEN","full_name":"Composite Adversarial Robustness Benchmark","num_papers_in_archive":2},{"url":"/dataset/chammi","name":"CHAMMI","full_name":"CHAMMI: A benchmark for channel-adaptive models in microscopy imaging","num_papers_in_archive":2},{"url":"/dataset/cross-view-time-dataset","name":"Cross-View Time Dataset","full_name":"","num_papers_in_archive":2},{"url":"/dataset/deep-pcb","name":"Deep PCB","full_name":"Deep Printed Circuit Board","num_papers_in_archive":2},{"url":"/dataset/df20-mini","name":"DF20 - Mini","full_name":"Danish Fungi 2020 - Mini","num_papers_in_archive":2},{"url":"/dataset/dib-10k","name":"DIB-10K","full_name":"DongNiao International Birds 10000","num_papers_in_archive":2},{"url":"/dataset/endotect-polyp-segmentation","name":"Endotect Polyp Segmentation Challenge Dataset","full_name":"","num_papers_in_archive":2},{"url":"/dataset/hiaml","name":"HiAML","full_name":"","num_papers_in_archive":2},{"url":"/dataset/inception","name":"Inception","full_name":"","num_papers_in_archive":2},{"url":"/dataset/invar-100","name":"InVar-100","full_name":"Industrial Objects in Varied Contexts","num_papers_in_archive":2},{"url":"/dataset/kvasir-capsule","name":"Kvasir-Capsule","full_name":"","num_papers_in_archive":2},{"url":"/dataset/mame","name":"MAMe","full_name":"Museum Art Medium dataset","num_papers_in_archive":2},{"url":"/dataset/new-plant-diseases-dataset","name":"New Plant Diseases Dataset","full_name":"Image dataset containing different healthy and unhealthy crop leaves.","num_papers_in_archive":2},{"url":"/dataset/polsf","name":"PolSF","full_name":"","num_papers_in_archive":2},{"url":"/dataset/s2rda","name":"S2RDA","full_name":"","num_papers_in_archive":2},{"url":"/dataset/si-score-1","name":"SI-Score","full_name":"","num_papers_in_archive":2},{"url":"/dataset/stir","name":"STIR","full_name":"Scaled and Translated Image Recognition","num_papers_in_archive":2},{"url":"/dataset/two-path","name":"Two-Path","full_name":"","num_papers_in_archive":2},{"url":"/dataset/ultra-fine-grained-leaves-cotton-soyageing","name":"Ultra Fine-Grained Leaves (Cotton, SoyAgeing, SoyGene, SoyGlobal, SoyLocal)","full_name":"","num_papers_in_archive":2},{"url":"/dataset/vistas-np","name":"Vistas-NP","full_name":null,"num_papers_in_archive":2},{"url":"/dataset/aiderv2","name":"AIDERV2","full_name":"Aerial Image Dataset for Emergency Response Applications (version 2)","num_papers_in_archive":1},{"url":"/dataset/ajwa-or-medjool","name":"AjwaOrMedjool","full_name":"AjwaOrMedjool: a binary balanced dataset to teach machine learning‏","num_papers_in_archive":1},{"url":"/dataset/arc-100","name":"ARC-100","full_name":"","num_papers_in_archive":1},{"url":"/dataset/artdl","name":"ArtDL","full_name":"ArtDL","num_papers_in_archive":1},{"url":"/dataset/banapple","name":"Banapple","full_name":"","num_papers_in_archive":1},{"url":"/dataset/banknote-net","name":"BankNote-Net","full_name":"","num_papers_in_archive":1},{"url":"/dataset/cervix93-cytology-dataset","name":"Cervix93 Cytology Dataset","full_name":null,"num_papers_in_archive":1},{"url":"/dataset/cleanstl-10","name":"CleanSTL-10","full_name":"","num_papers_in_archive":1},{"url":"/dataset/cnfood-241","name":"CNFOOD-241","full_name":"","num_papers_in_archive":1},{"url":"/dataset/cnfood-241-chen","name":"CNFOOD-241-Chen","full_name":"CNFOOD-241-Chen","num_papers_in_archive":1},{"url":"/dataset/cross-view-time-dataset-cross-camera-split","name":"Cross-View Time Dataset (Cross-Camera Split)","full_name":"","num_papers_in_archive":1},{"url":"/dataset/hamdan-gani","name":"Cultural Events Classification using Hyper-parameter Optimization of Deep Learning Technique","full_name":"","num_papers_in_archive":1},{"url":"/dataset/dallestreet","name":"DalleStreet","full_name":"","num_papers_in_archive":1},{"url":"/dataset/dry-bean-dataset","name":"Dry Bean Dataset","full_name":"","num_papers_in_archive":1},{"url":"/dataset/earlynsd","name":"EarlyNSD","full_name":"Early Nutrient Stress Detection of Plants","num_papers_in_archive":1},{"url":"/dataset/enseg","name":"ENSeg","full_name":"","num_papers_in_archive":1},{"url":"/dataset/fathomnet2023","name":"FathomNet2023","full_name":"FathomNet2023 Competition Dataset","num_papers_in_archive":1},{"url":"/dataset/fruits-dataset-for-classification","name":"Fruits Dataset for Classification","full_name":"","num_papers_in_archive":1},{"url":"/dataset/gaze-cifar-10","name":"Gaze-CIFAR-10","full_name":"","num_papers_in_archive":1},{"url":"/dataset/hasper","name":"HaSPeR","full_name":"Hand Shadow Puppet Image Repository","num_papers_in_archive":1},{"url":"/dataset/icon645","name":"Icon645","full_name":"","num_papers_in_archive":1},{"url":"/dataset/id-pattern-dataset","name":"Id Pattern Dataset","full_name":"","num_papers_in_archive":1},{"url":"/dataset/idsprites","name":"idsprites","full_name":"Infinite dSprites","num_papers_in_archive":1},{"url":"/dataset/imagenet-50-samples-per-class","name":"ImageNet 50 samples per class","full_name":"","num_papers_in_archive":1},{"url":"/dataset/impact-patent","name":"IMPACT Patent","full_name":"A Large-scale Integrated Multimodal Patent Analysis and Creation Dataset for Design Patents","num_papers_in_archive":1},{"url":"/dataset/inaturalist-fine-grained-geolocation","name":"iNaturalist Fine-Grained Geolocation","full_name":null,"num_papers_in_archive":1},{"url":"/dataset/indian-party-symbol-dataset","name":"Indian Party Symbol Dataset","full_name":"","num_papers_in_archive":1},{"url":"/dataset/iran-s-built-heritage-binary-image","name":"Iran's Built Heritage Binary Image Classification Dataset","full_name":"","num_papers_in_archive":1},{"url":"/dataset/irma","name":"IRMA","full_name":"15,363 IRMA images of 193 categories for ImageCLEFmed 2009","num_papers_in_archive":1},{"url":"/dataset/isbnet","name":"ISBNet","full_name":"","num_papers_in_archive":1},{"url":"/dataset/jambo","name":"JAMBO","full_name":"A Multi-Annotator Image Dataset for Benthic Habitat Classification","num_papers_in_archive":1},{"url":"/dataset/labels","name":"Labels","full_name":"","num_papers_in_archive":1},{"url":"/dataset/laofiw-dataset","name":"LAOFIW Dataset","full_name":"Labeled Ancestral Origin Faces in the Wild","num_papers_in_archive":1},{"url":"/dataset/large-labelled-logo-dataset-l3d","name":"Large Labelled Logo Dataset (L3D)","full_name":"","num_papers_in_archive":1},{"url":"/dataset/leafnet","name":"LeafNet","full_name":"LeafNet: A large-scale dataset for training image-text models in leaf disease identification","num_papers_in_archive":1},{"url":"/dataset/mapreader-data","name":"MapReader Data","full_name":"in GeoHumanities workshop, SIGSPATIAL 2022","num_papers_in_archive":1},{"url":"/dataset/mumin-large","name":"MuMiN-large","full_name":"","num_papers_in_archive":1},{"url":"/dataset/mumin-medium","name":"MuMiN-medium","full_name":"","num_papers_in_archive":1},{"url":"/dataset/mumin-small","name":"MuMiN-small","full_name":"","num_papers_in_archive":1},{"url":"/dataset/no-background-rgb-arabic-alphabets-sign","name":"No Background RGB Arabic Alphabets Sign Language Dataset","full_name":"","num_papers_in_archive":1},{"url":"/dataset/notre-dame-cathedral-fire","name":"Notre-Dame Cathedral Fire","full_name":"","num_papers_in_archive":1},{"url":"/dataset/olid-i","name":"OLID I","full_name":"An Open Leaf Image Dataset of Bangladesh's Major Crops","num_papers_in_archive":1},{"url":"/dataset/omni-image","name":"Omni-Image","full_name":"","num_papers_in_archive":1},{"url":"/dataset/openstreetmap-multi-sensor-scene","name":"OpenStreetMap Multi-Sensor Scene Classification","full_name":"","num_papers_in_archive":1},{"url":"/dataset/orchid2024","name":"Orchid2024","full_name":"","num_papers_in_archive":1},{"url":"/dataset/photozilla","name":"Photozilla","full_name":"","num_papers_in_archive":1},{"url":"/dataset/potato-a-dataset-for-analyzing-polarimetric","name":"PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects","full_name":"PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects","num_papers_in_archive":1},{"url":"/dataset/prima","name":"PRImA","full_name":"PRImA","num_papers_in_archive":1},{"url":"/dataset/rgb-arabic-alphabet-sign-language-aasl","name":"RGB Arabic Alphabet Sign Language (AASL) dataset","full_name":"RGB Arabic Alphabet Sign Language (AASL) dataset","num_papers_in_archive":1},{"url":"/dataset/shipspotting","name":"ShipSpotting","full_name":"","num_papers_in_archive":1},{"url":"/dataset/solardk","name":"SolarDK","full_name":"","num_papers_in_archive":1},{"url":"/dataset/spot-10","name":"SPOT-10","full_name":"Animal Pattern Benchmark Dataset for Machine Learning Algorithms","num_papers_in_archive":1},{"url":"/dataset/ssbi-dataset","name":"SSBI Dataset","full_name":"Synthetic Signature Bankcheck Images","num_papers_in_archive":1},{"url":"/dataset/susy-dataset","name":"SuSy Dataset","full_name":"","num_papers_in_archive":1},{"url":"/dataset/svld","name":"SVLD","full_name":"Social Vision and Language Dataset","num_papers_in_archive":1},{"url":"/dataset/synthetic-covid-19-cxr-dataset","name":"Synthetic COVID-19 CXR Dataset","full_name":"","num_papers_in_archive":1},{"url":"/dataset/tcb-ds","name":"TCB-DS","full_name":"Toxigenic Cyanobacteria Dataset","num_papers_in_archive":1},{"url":"/dataset/tem-nanowire-morphologies-for-classification","name":"TEM nanowire morphologies for classification and segmentation","full_name":"Transmission electron microscopy (TEM) image datasets of peptide / protein nanowire morphologies","num_papers_in_archive":1},{"url":"/dataset/topex-printer","name":"topex-printer","full_name":"","num_papers_in_archive":1},{"url":"/dataset/tsinghua-dogs","name":"Tsinghua Dogs","full_name":"","num_papers_in_archive":1},{"url":"/dataset/twinsynths","name":"TwinSynths","full_name":"","num_papers_in_archive":1},{"url":"/dataset/weapon-detection-dataset","name":"Weapon Detection Dataset","full_name":"","num_papers_in_archive":1},{"url":"/dataset/yfcc100m-fine-grained-geolocation","name":"YFCC100M Fine-Grained Geolocation","full_name":null,"num_papers_in_archive":1},{"url":"/dataset/adfi-dataset-anomaly-detection-dataset","name":"ADFI","full_name":"Anomaly Detection Datasets for Visual Inspection","num_papers_in_archive":0},{"url":"/dataset/alpaca-dataset-image-classification","name":"Alpaca Dataset Image Classification","full_name":"","num_papers_in_archive":0},{"url":"/dataset/applescabfds","name":"AppleScabFDs","full_name":"","num_papers_in_archive":0},{"url":"/dataset/applescablds","name":"AppleScabLDs","full_name":"","num_papers_in_archive":0},{"url":"/dataset/ccic","name":"CCIC","full_name":"Concrete Crack Images for Classification","num_papers_in_archive":0},{"url":"/dataset/ceahb2021-5","name":"CEAHB2021-5","full_name":"Chinese Ethnic Ancient Handwritten Books database","num_papers_in_archive":0},{"url":"/dataset/corn-kernel-images-dataset","name":"Corn Kernel Images Dataset","full_name":"","num_papers_in_archive":0},{"url":"/dataset/corn-seeds-dataset","name":"Corn Seeds Dataset","full_name":"","num_papers_in_archive":0},{"url":"/dataset/eapplescab","name":"eAppleScab","full_name":"Apple Scab in the Early Stage of Development","num_papers_in_archive":0},{"url":"/dataset/four-shapes","name":"Four Shapes","full_name":"","num_papers_in_archive":0},{"url":"/dataset/lusitano-fabric-defect-detection-dataset","name":"Lusitano Fabric Defect Detection Dataset","full_name":"Lusitano Fabric Defect Detection Dataset","num_papers_in_archive":0},{"url":"/dataset/moroccan-monay-dataset","name":"Moroccan Monay dataset","full_name":"","num_papers_in_archive":0},{"url":"/dataset/mudestreda","name":"Mudestreda","full_name":"Mudestreda Multimodal Device State Recognition Dataset","num_papers_in_archive":0},{"url":"/dataset/portuguese-meals-dataset","name":"Portuguese Meals Dataset","full_name":"","num_papers_in_archive":0},{"url":"/dataset/sakha-tb","name":"Sakha-TB","full_name":"400+400 CXR images for TB diagnosis","num_papers_in_archive":0},{"url":"/dataset/tcmp-300","name":"TCMP-300","full_name":"Traditional Chinese Medicinal Plant Dataset","num_papers_in_archive":0}],"subtasks":[{"url":"/task/artist-classification","name":"Artist classification"},{"url":"/task/artistic-style-classification","name":"Artistic style classification"},{"url":"/task/classification-consistency","name":"Classification Consistency"},{"url":"/task/concept-based-classification","name":"Concept-based Classification"},{"url":"/task/document-image-classification","name":"Document Image Classification"},{"url":"/task/efficient-vits","name":"Efficient ViTs"},{"url":"/task/few-shot-image-classification","name":"Few-Shot Image Classification"},{"url":"/task/fine-grained-image-classification","name":"Fine-Grained Image Classification"},{"url":"/task/fruit-type-maturity-state-prediction-multi","name":"Fruit-type + Maturity-state Prediction (Multi-label Classifivation)"},{"url":"/task/fruit-type-maturity-state-prediction-multi-1","name":"Fruit-type + Maturity-state Prediction (Multi-label Classification)"},{"url":"/task/gallbladder-cancer-detection","name":"Gallbladder Cancer Detection"},{"url":"/task/genre-classification","name":"Genre classification"},{"url":"/task/hyperspectral-image-classification","name":"Hyperspectral Image Classification"},{"url":"/task/image-classification-with-dp","name":"Image Classification with Differential Privacy"},{"url":"/task/learning-with-noisy-labels","name":"Learning with noisy labels"},{"url":"/task/misclassification-rate-natural-adversarial","name":"Misclassification Rate - Natural Adversarial Samples"},{"url":"/task/multi-label-image-classification","name":"Multi-Label Image Classification"},{"url":"/task/multi-label-image-recognition","name":"Multi-Label Image Recognition"},{"url":"/task/ood-detection","name":"Out of Distribution (OOD) Detection"},{"url":"/task/photo-geolocation-estimation","name":"Photo geolocation estimation"},{"url":"/task/railway-track-image-classification","name":"Railway Track Image Classification"},{"url":"/task/raw-vs-ripe-generic","name":"Raw vs Ripe (Generic)"},{"url":"/task/satellite-image-classification","name":"Satellite Image Classification"},{"url":"/task/scale-generalisation","name":"Scale Generalisation"},{"url":"/task/self-supervised-image-classification","name":"Self-Supervised Image Classification"},{"url":"/task/semi-supervised-image-classification","name":"Semi-Supervised Image Classification"},{"url":"/task/sequential-image-classification","name":"Sequential Image Classification"},{"url":"/task/small-data","name":"Small Data Image Classification"},{"url":"/task/sparse-representation-based-classification","name":"Sparse Representation-based Classification"},{"url":"/task/superpixel-image-classification","name":"Superpixel Image Classification"},{"url":"/task/temporal-metadata-manipulation-detection","name":"Temporal Metadata Manipulation Detection"},{"url":"/task/token-reduction","name":"Token Reduction"},{"url":"/task/unsupervised-image-classification","name":"Unsupervised Image Classification"}],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":4702,"tagged_in_all":10488,"items":[{"url":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","arxiv_id":"1512.03385","repositories_listed":484,"syntology":{"n":377,"n_ran":230,"n_unverified":147,"n_pointer_only":187}},{"url":"/paper/very-deep-convolutional-networks-for-large","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","date":"2014-09-04","arxiv_id":"1409.1556","repositories_listed":305,"syntology":{"n":122,"n_ran":12,"n_unverified":110,"n_pointer_only":4}},{"url":"/paper/mobilenetv2-inverted-residuals-and-linear","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","date":"2018-01-13","arxiv_id":"1801.04381","repositories_listed":159,"syntology":{"n":111,"n_ran":85,"n_unverified":26,"n_pointer_only":64}},{"url":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","arxiv_id":"2010.11929","repositories_listed":158,"syntology":{"n":419,"n_ran":281,"n_unverified":138,"n_pointer_only":154}},{"url":"/paper/densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","arxiv_id":"1608.06993","repositories_listed":146,"syntology":{"n":71,"n_ran":18,"n_unverified":53,"n_pointer_only":7}},{"url":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","arxiv_id":"1905.11946","repositories_listed":144,"syntology":{"n":302,"n_ran":171,"n_unverified":131,"n_pointer_only":112}},{"url":"/paper/grad-cam-visual-explanations-from-deep","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","date":"2016-10-07","arxiv_id":"1610.02391","repositories_listed":126,"syntology":{"n":141,"n_ran":79,"n_unverified":62,"n_pointer_only":68}},{"url":"/paper/grad-cam-visual-explanations-from-deep","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","date":"2016-10-07","arxiv_id":"1610.02391","repositories_listed":126,"syntology":{"n":141,"n_ran":79,"n_unverified":62,"n_pointer_only":68}},{"url":"/paper/cspnet-a-new-backbone-that-can-enhance","title":"CSPNet: A New Backbone that can Enhance Learning Capability of CNN","date":"2019-11-27","arxiv_id":"1911.11929","repositories_listed":123,"syntology":null},{"url":"/paper/rethinking-the-inception-architecture-for","title":"Rethinking the Inception Architecture for Computer Vision","date":"2015-12-02","arxiv_id":"1512.00567","repositories_listed":113,"syntology":{"n":26,"n_ran":5,"n_unverified":21,"n_pointer_only":4}},{"url":"/paper/a-simple-framework-for-contrastive-learning","title":"A Simple Framework for Contrastive Learning of Visual Representations","date":"2020-02-13","arxiv_id":"2002.05709","repositories_listed":96,"syntology":{"n":137,"n_ran":79,"n_unverified":58,"n_pointer_only":52}},{"url":"/paper/inception-v4-inception-resnet-and-the-impact","title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","date":"2016-02-23","arxiv_id":"1602.07261","repositories_listed":87,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/squeeze-and-excitation-networks","title":"Squeeze-and-Excitation Networks","date":"2017-09-05","arxiv_id":"1709.01507","repositories_listed":85,"syntology":{"n":9,"n_ran":2,"n_unverified":7,"n_pointer_only":6}},{"url":"/paper/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","arxiv_id":"1703.03400","repositories_listed":85,"syntology":{"n":154,"n_ran":86,"n_unverified":68,"n_pointer_only":57}},{"url":"/paper/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","arxiv_id":"1703.03400","repositories_listed":85,"syntology":{"n":154,"n_ran":86,"n_unverified":68,"n_pointer_only":57}},{"url":"/paper/going-deeper-with-convolutions","title":"Going Deeper with Convolutions","date":"2014-09-17","arxiv_id":"1409.4842","repositories_listed":83,"syntology":{"n":42,"n_ran":27,"n_unverified":15,"n_pointer_only":20}},{"url":"/paper/learning-transferable-visual-models-from","title":"Learning Transferable Visual Models From Natural Language Supervision","date":"2021-02-26","arxiv_id":"2103.00020","repositories_listed":82,"syntology":{"n":20,"n_ran":16,"n_unverified":4,"n_pointer_only":16}},{"url":"/paper/swin-transformer-hierarchical-vision","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","date":"2021-03-25","arxiv_id":"2103.14030","repositories_listed":80,"syntology":{"n":207,"n_ran":108,"n_unverified":99,"n_pointer_only":43}},{"url":"/paper/swin-transformer-hierarchical-vision","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","date":"2021-03-25","arxiv_id":"2103.14030","repositories_listed":80,"syntology":{"n":207,"n_ran":108,"n_unverified":99,"n_pointer_only":43}},{"url":"/paper/encoder-decoder-with-atrous-separable","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","date":"2018-02-07","arxiv_id":"1802.02611","repositories_listed":78,"syntology":{"n":72,"n_ran":43,"n_unverified":29,"n_pointer_only":40}},{"url":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","arxiv_id":"1710.09829","repositories_listed":77,"syntology":{"n":118,"n_ran":18,"n_unverified":100,"n_pointer_only":12}},{"url":"/paper/wide-residual-networks","title":"Wide Residual Networks","date":"2016-05-23","arxiv_id":"1605.07146","repositories_listed":72,"syntology":{"n":96,"n_ran":60,"n_unverified":36,"n_pointer_only":46}},{"url":"/paper/mixup-beyond-empirical-risk-minimization","title":"mixup: Beyond Empirical Risk Minimization","date":"2017-10-25","arxiv_id":"1710.09412","repositories_listed":71,"syntology":{"n":47,"n_ran":30,"n_unverified":17,"n_pointer_only":15}},{"url":"/paper/batch-normalization-accelerating-deep-network","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","date":"2015-02-11","arxiv_id":"1502.03167","repositories_listed":70,"syntology":{"n":21,"n_ran":14,"n_unverified":7,"n_pointer_only":5}},{"url":"/paper/batch-normalization-accelerating-deep-network","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","date":"2015-02-11","arxiv_id":"1502.03167","repositories_listed":70,"syntology":{"n":21,"n_ran":14,"n_unverified":7,"n_pointer_only":5}},{"url":"/paper/searching-for-mobilenetv3","title":"Searching for MobileNetV3","date":"2019-05-06","arxiv_id":"1905.02244","repositories_listed":67,"syntology":{"n":105,"n_ran":58,"n_unverified":47,"n_pointer_only":46}},{"url":"/paper/pyramid-scene-parsing-network","title":"Pyramid Scene Parsing Network","date":"2016-12-04","arxiv_id":"1612.01105","repositories_listed":67,"syntology":{"n":29,"n_ran":7,"n_unverified":22,"n_pointer_only":5}},{"url":"/paper/aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","arxiv_id":"1611.05431","repositories_listed":61,"syntology":{"n":80,"n_ran":34,"n_unverified":46,"n_pointer_only":13}},{"url":"/paper/aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","arxiv_id":"1611.05431","repositories_listed":61,"syntology":{"n":80,"n_ran":34,"n_unverified":46,"n_pointer_only":13}},{"url":"/paper/darts-differentiable-architecture-search","title":"DARTS: Differentiable Architecture Search","date":"2018-06-24","arxiv_id":"1806.09055","repositories_listed":59,"syntology":{"n":156,"n_ran":66,"n_unverified":90,"n_pointer_only":48}}],"syntology_records":29,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":2,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}