{"url":"/dataset/nct-crc-he-100k","name":"NCT-CRC-HE-100K","full_name":null,"description_markdown":"The NCT-CRC-HE-100K dataset is a set of 100,000 non-overlapping image patches extracted from 86 H$\\&$E stained human cancer tissue slides and normal tissue from the NCT biobank (National Center for Tumor Diseases) and the UMM pathology archive (University Medical Center Mannheim). While the dataset Colorectal Cacner-Validation-Histology-7K (CRC-VAL-HE-7K) consist of 7180 images extracted from 50 patients with colorectal adenocarcinoma and were used to create a dataset that does not overlap with patients in the NCT-CRC-HE-100K dataset. It was created by pathologists by manually delineating tissue regions in whole slide images into the following nine tissue classes: Adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium (TUM).\r\n\r\nImage source: [https://www.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf](https://www.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf)","description_withheld":null,"homepage":"https://zenodo.org/record/1214456","introduced_date":"2018-04-07","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Medical Image Classification","url":"/task/medical-image-classification","datasets_with_task":"/datasets/task/medical-image-classification"}],"languages":[],"variants":["NCT-CRC-HE-100K"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/1aurent/NCT-CRC-HE","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-classification-on-nct-crc-he","task":"Medical Image Classification","dataset_variant":"NCT-CRC-HE-100K","rows":7,"metrics":["Accuracy (%)","F1-Score","Precision","Specificity"],"first_row_in_archive_order":{"model":"Efficientnet-b0","paper":"/paper/efficientnet-rethinking-model-scaling-for","metrics":{"Accuracy (%)":"95.59","F1-Score":"97.48","Precision":"99.89","Specificity":"99.45"},"code_links":[{"title":"ultralytics/yolov5","url":"https://github.com/ultralytics/yolov5"},{"title":"rwightman/pytorch-image-models","url":"https://github.com/rwightman/pytorch-image-models"},{"title":"open-mmlab/mmdetection","url":"https://github.com/open-mmlab/mmdetection"},{"title":"pytorch/vision","url":"https://github.com/pytorch/vision"},{"title":"NVIDIA/DeepLearningExamples","url":"https://github.com/NVIDIA/DeepLearningExamples"},{"title":"PaddlePaddle/PaddleDetection","url":"https://github.com/PaddlePaddle/PaddleDetection"},{"title":"lukemelas/EfficientNet-PyTorch","url":"https://github.com/lukemelas/EfficientNet-PyTorch"},{"title":"PaddlePaddle/PaddleClas","url":"https://github.com/PaddlePaddle/PaddleClas"},{"title":"tensorflow/tpu","url":"https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet"},{"title":"Deci-AI/super-gradients","url":"https://github.com/Deci-AI/super-gradients"},{"title":"osmr/imgclsmob","url":"https://github.com/osmr/imgclsmob"},{"title":"facebookresearch/pycls","url":"https://github.com/facebookresearch/pycls"},{"title":"qubvel/efficientnet","url":"https://github.com/qubvel/efficientnet"},{"title":"facebookresearch/ClassyVision","url":"https://github.com/facebookresearch/ClassyVision"},{"title":"rwightman/gen-efficientnet-pytorch","url":"https://github.com/rwightman/gen-efficientnet-pytorch"},{"title":"open-edge-platform/training_extensions","url":"https://github.com/open-edge-platform/training_extensions"},{"title":"christiansafka/img2vec","url":"https://github.com/christiansafka/img2vec"},{"title":"clovaai/rexnet","url":"https://github.com/clovaai/rexnet"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/blob/master/research/cv/efficientnet-b0"},{"title":"open-edge-platform/geti","url":"https://github.com/open-edge-platform/geti"},{"title":"github-luffy/PFLD_68points_Pytorch","url":"https://github.com/github-luffy/PFLD_68points_Pytorch"},{"title":"zsef123/EfficientNets-PyTorch","url":"https://github.com/zsef123/EfficientNets-PyTorch"},{"title":"mindspore-ecosystem/mindcv","url":"https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/efficientnet.py"},{"title":"Mayurji/Image-Classification-PyTorch","url":"https://github.com/Mayurji/Image-Classification-PyTorch"},{"title":"titu1994/keras-efficientnets","url":"https://github.com/titu1994/keras-efficientnets"},{"title":"shijianjian/efficientnet-pytorch-3d","url":"https://github.com/shijianjian/efficientnet-pytorch-3d"},{"title":"Tirth27/Skin-Cancer-Classification-using-Deep-Learning","url":"https://github.com/Tirth27/Skin-Cancer-Classification-using-Deep-Learning"},{"title":"rwightman/efficientnet-jax","url":"https://github.com/rwightman/efficientnet-jax"},{"title":"narumiruna/efficientnet-pytorch","url":"https://github.com/narumiruna/efficientnet-pytorch"},{"title":"mingxingtan/efficientnet","url":"https://github.com/mingxingtan/efficientnet"},{"title":"wusaifei/HWCC_image_classification","url":"https://github.com/wusaifei/HWCC_image_classification"},{"title":"federicopozzi33/MobileOne-PyTorch","url":"https://github.com/federicopozzi33/MobileOne-PyTorch"},{"title":"canturan10/satellighte","url":"https://github.com/canturan10/satellighte"},{"title":"morganmcg1/stanford-cars","url":"https://github.com/morganmcg1/stanford-cars"},{"title":"abhuse/pytorch-efficientnet","url":"https://github.com/abhuse/pytorch-efficientnet"},{"title":"james77777778/keras-image-models","url":"https://github.com/james77777778/keras-image-models"},{"title":"captaindario/dakanji-single-kanji-recognition","url":"https://github.com/captaindario/dakanji-single-kanji-recognition"},{"title":"gouthamvgk/coreml_conversion_hub","url":"https://github.com/gouthamvgk/coreml_conversion_hub"},{"title":"megvii-research/basecls","url":"https://github.com/megvii-research/basecls/tree/main/zoo/public/effnet"},{"title":"mnikitin/EfficientNet","url":"https://github.com/mnikitin/EfficientNet"},{"title":"gomezzz/MSMatch","url":"https://github.com/gomezzz/MSMatch"},{"title":"filaPro/visda2019","url":"https://github.com/filaPro/visda2019"},{"title":"AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0","url":"https://github.com/AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0"},{"title":"jaketae/mlp-mixer","url":"https://github.com/jaketae/mlp-mixer"},{"title":"ckyrkou/EmergencyNet","url":"https://github.com/ckyrkou/EmergencyNet"},{"title":"birder/birder","url":"https://gitlab.com/birder/birder"},{"title":"tsing-cv/EfficientNet-tensorflow-eager","url":"https://github.com/tsing-cv/EfficientNet-tensorflow-eager"},{"title":"marcointrovigne/WeatherDetection","url":"https://github.com/marcointrovigne/WeatherDetection"},{"title":"mindspore-courses/External-Attention-MindSpore","url":"https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/conv/MBConv.py"},{"title":"DableUTeeF/keras-efficientnet","url":"https://github.com/DableUTeeF/keras-efficientnet"},{"title":"ZackPashkin/YOLOv3-EfficientNet-EffYolo","url":"https://github.com/ZackPashkin/YOLOv3-EfficientNet-EffYolo"},{"title":"buiquangmanhhp1999/age_gender_estimation","url":"https://github.com/buiquangmanhhp1999/age_gender_estimation"},{"title":"rajneeshaggarwal/google-efficientnet","url":"https://github.com/rajneeshaggarwal/google-efficientnet"},{"title":"rohitgr7/tvmodels","url":"https://github.com/rohitgr7/tvmodels"},{"title":"Jintao-Huang/EfficientNet_PyTorch","url":"https://github.com/Jintao-Huang/EfficientNet_PyTorch"},{"title":"ravi02512/efficientdet-keras","url":"https://github.com/ravi02512/efficientdet-keras"},{"title":"kdha0727/lung-opacity-and-covid-chest-x-ray-detection","url":"https://github.com/kdha0727/lung-opacity-and-covid-chest-x-ray-detection"},{"title":"BobMcDear/pytorch-efficientnet","url":"https://github.com/BobMcDear/pytorch-efficientnet"},{"title":"vladthesav/MoldAI","url":"https://github.com/vladthesav/MoldAI"},{"title":"morrisxu-driving/video-music_cross-modal_retrival","url":"https://github.com/morrisxu-driving/video-music_cross-modal_retrival"},{"title":"maxwelltsai/DeepGalaxy","url":"https://github.com/maxwelltsai/DeepGalaxy"},{"title":"IMvision12/keras-vision-models","url":"https://github.com/IMvision12/keras-vision-models"},{"title":"linhduongtuan/Fruits_Vegetables_Classifier_WebApp","url":"https://github.com/linhduongtuan/Fruits_Vegetables_Classifier_WebApp"},{"title":"iamilyasedunov/key_word_spotting","url":"https://github.com/iamilyasedunov/key_word_spotting"},{"title":"ChintanThacker/Facial_Expression_EfficientNet","url":"https://github.com/ChintanThacker/Facial_Expression_EfficientNet"},{"title":"toanbkmt/EfficientnetFruitDetect","url":"https://github.com/toanbkmt/EfficientnetFruitDetect"},{"title":"kairess/efficientnet_example","url":"https://github.com/kairess/efficientnet_example"},{"title":"wangyi111/international-archaeology-ai-challenge","url":"https://github.com/wangyi111/international-archaeology-ai-challenge"},{"title":"triple7/Keras-WGAN-RGB-128x128","url":"https://github.com/triple7/Keras-WGAN-RGB-128x128"},{"title":"Burf/EfficientNet-Lite-Tensorflow2","url":"https://github.com/Burf/EfficientNet-Lite-Tensorflow2"},{"title":"reyvaz/steel-defect-segmentation","url":"https://github.com/reyvaz/steel-defect-segmentation"},{"title":"reyvaz/pneumothorax_detection","url":"https://github.com/reyvaz/pneumothorax_detection"},{"title":"armin-azh/3DefficientNet","url":"https://github.com/armin-azh/3DefficientNet"},{"title":"pikkaay/efficientnet_gpu","url":"https://github.com/pikkaay/efficientnet_gpu"},{"title":"BenjiKCF/EfficientNet","url":"https://github.com/BenjiKCF/EfficientNet"},{"title":"2024-MindSpore-1/Code2","url":"https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/efficientnet"},{"title":"js-aguiar/wheat-object-detection","url":"https://github.com/js-aguiar/wheat-object-detection"},{"title":"sandipsahajoy/Predicting-Lymphoma-using-CNN-in-Keras","url":"https://github.com/sandipsahajoy/Predicting-Lymphoma-using-CNN-in-Keras"},{"title":"amirdy/dog-breed-classification","url":"https://github.com/amirdy/dog-breed-classification"},{"title":"MindSpore-paper-code-3/code2","url":"https://github.com/MindSpore-paper-code-3/code2/tree/main/darknet53"},{"title":"huynhtuan17ti/AI4VN-Hackathon2020","url":"https://github.com/huynhtuan17ti/AI4VN-Hackathon2020"},{"title":"miramind/efficientnets_pytorch","url":"https://github.com/miramind/efficientnets_pytorch"},{"title":"danielpatrickhug/Research_Paper_Parser","url":"https://github.com/danielpatrickhug/Research_Paper_Parser"},{"title":"chrisqqq123/FA-Dist-EfficientNet","url":"https://github.com/chrisqqq123/FA-Dist-EfficientNet"},{"title":"HyeonhoonLee/MAIC2021_Sleep","url":"https://github.com/HyeonhoonLee/MAIC2021_Sleep"},{"title":"xslidi/EfficientNets_ddl_apex","url":"https://github.com/xslidi/EfficientNets_ddl_apex"},{"title":"JacobM184/EfficientNet-for-Gun-detection","url":"https://github.com/JacobM184/EfficientNet-for-Gun-detection"},{"title":"WonJunPark/Efficientnet","url":"https://github.com/WonJunPark/Efficientnet"},{"title":"VinayBhupalam/melanoma-detection","url":"https://github.com/VinayBhupalam/melanoma-detection"},{"title":"xiuyu0000/vision","url":"https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/backbones/efficientnet.py"},{"title":"tarikdgny/Optical_Coherence_Tomography","url":"https://github.com/tarikdgny/Optical_Coherence_Tomography"},{"title":"northeastsquare/effficientnet","url":"https://github.com/northeastsquare/effficientnet"},{"title":"konstantinos-p/image_classification_SOTA","url":"https://github.com/konstantinos-p/image_classification_SOTA"},{"title":"PotatoSpudowski/CactiNet","url":"https://github.com/PotatoSpudowski/CactiNet"},{"title":"HO4X/TSR_JetsonTX2","url":"https://github.com/HO4X/TSR_JetsonTX2"},{"title":"jason90330/EdgeFinal","url":"https://github.com/jason90330/EdgeFinal"},{"title":"youngwoo-yoon/Practical-tips-for-lightweight-deep-learning","url":"https://github.com/youngwoo-yoon/Practical-tips-for-lightweight-deep-learning"},{"title":"filipmu/Kaggle-APTOS-2019-Blindness","url":"https://github.com/filipmu/Kaggle-APTOS-2019-Blindness"},{"title":"nimiew/Grab-Computer-Vision","url":"https://github.com/nimiew/Grab-Computer-Vision"},{"title":"DeepBrainsMe/FSnet","url":"https://github.com/DeepBrainsMe/FSnet"},{"title":"maragori/DeepfakeForensics-v1","url":"https://github.com/maragori/DeepfakeForensics-v1"},{"title":"hamed-ahangari/Separation-Index-of-convolutional-layers-in-EfficientNet-B0","url":"https://github.com/hamed-ahangari/Separation-Index-of-convolutional-layers-in-EfficientNet-B0"},{"title":"najlaeLemrabet/FacialKeypointsDetection","url":"https://github.com/najlaeLemrabet/FacialKeypointsDetection"},{"title":"alililia/ms_extend","url":"https://github.com/alililia/ms_extend/tree/main/gpu_efficientnet"},{"title":"rmwkwok/product_visual_search","url":"https://github.com/rmwkwok/product_visual_search"},{"title":"asad-62/IVP-DNN","url":"https://github.com/asad-62/IVP-DNN"},{"title":"JoegameZhou/efficientnet-b0","url":"https://github.com/JoegameZhou/efficientnet-b0"},{"title":"lyqcom/efficientnet","url":"https://github.com/lyqcom/efficientnet"},{"title":"Mind23-2/MindCode-37","url":"https://github.com/Mind23-2/MindCode-37"},{"title":"6210612757/facerecognition","url":"https://github.com/6210612757/facerecognition"},{"title":"darya-baranovskaya/keyword_spotting","url":"https://github.com/darya-baranovskaya/keyword_spotting"},{"title":"2023-MindSpore-1/ms-code-195","url":"https://github.com/2023-MindSpore-1/ms-code-195"},{"title":"2023-MindSpore-1/ms-code-151","url":"https://github.com/2023-MindSpore-1/ms-code-151"},{"title":"2023-MindSpore-1/ms-code-189","url":"https://github.com/2023-MindSpore-1/ms-code-189/tree/main/modelarts"},{"title":"Cyprien0105/DataScience","url":"https://github.com/Cyprien0105/DataScience"},{"title":"TravisLeeTS/grabcvchallenge","url":"https://github.com/TravisLeeTS/grabcvchallenge"},{"title":"seunghwan1228/CNN_EfficientNet","url":"https://github.com/seunghwan1228/CNN_EfficientNet"},{"title":"denizyuret/playground","url":"https://github.com/denizyuret/playground"},{"title":"MindSpore-paper-code-2/code400","url":"https://github.com/MindSpore-paper-code-2/code400/tree/main/Efficientnet"},{"title":"SunDoge/efficientnet-pytorch","url":"https://github.com/SunDoge/efficientnet-pytorch"},{"title":"machinelearning-goettingen/EfficientNet-ModelScaling","url":"https://github.com/machinelearning-goettingen/EfficientNet-ModelScaling"},{"title":"cgebbe/kaggle_pku-autonomous-driving","url":"https://github.com/cgebbe/kaggle_pku-autonomous-driving"},{"title":"2023-MindSpore-4/Code3","url":"https://github.com/2023-MindSpore-4/Code3/tree/main/efficientnet-b0"},{"title":"isaachaw/GrabCarRecognition","url":"https://github.com/isaachaw/GrabCarRecognition"},{"title":"MindSpore-paper-code-3/code8","url":"https://github.com/MindSpore-paper-code-3/code8/tree/main/darknet53"},{"title":"MindSpore-paper-code-3/code8","url":"https://github.com/MindSpore-paper-code-3/code8/tree/main/efficientnet-b0"},{"title":"code-implementation1/Code3","url":"https://github.com/code-implementation1/Code3/tree/main/efficientnet-b0"},{"title":"Legoons/Melanoma_classification","url":"https://github.com/Legoons/Melanoma_classification"},{"title":"SifatMd/Research-Papers","url":"https://github.com/SifatMd/Research-Papers"},{"title":"skhetarpal/Prostate_Cancer_Grade_Assessment","url":"https://github.com/skhetarpal/Prostate_Cancer_Grade_Assessment"},{"title":"setharram/facenet","url":"https://github.com/setharram/facenet"},{"title":"prakhargoyal106/MelanomaClassification","url":"https://github.com/prakhargoyal106/MelanomaClassification"},{"title":"luuchung/cifar-100","url":"https://github.com/luuchung/cifar-100"},{"title":"lvweiwolf/efficientdet","url":"https://github.com/lvweiwolf/efficientdet"},{"title":"mvenouziou/Project-Attention-Is-What-You-Get","url":"https://github.com/mvenouziou/Project-Attention-Is-What-You-Get"},{"title":"semskurto/APTOS","url":"https://github.com/semskurto/APTOS"},{"title":"Jmak12/Iris1","url":"https://github.com/Jmak12/Iris1"},{"title":"hyang0129/foodclassapp","url":"https://github.com/hyang0129/foodclassapp"},{"title":"huzpsb/EffTransfer","url":"https://github.com/huzpsb/EffTransfer"},{"title":"epoc88/PFLD_68pts_Pytorch_2020","url":"https://github.com/epoc88/PFLD_68pts_Pytorch_2020"},{"title":"lpirola13/flower_recognizer","url":"https://github.com/lpirola13/flower_recognizer"},{"title":"gomezzz/distmsmatch","url":"https://github.com/gomezzz/distmsmatch"},{"title":"lpirola13/flower-recognizer","url":"https://github.com/lpirola13/flower-recognizer"},{"title":"houstonsantos/CassavaLeafDisease","url":"https://github.com/houstonsantos/CassavaLeafDisease"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-classification-on-nct-crc-he-100k","task":"Image Classification","dataset_variant":"NCT-CRC-HE-100K","rows":1,"metrics":["F1"],"first_row_in_archive_order":{"model":"SAG-ViT","paper":"/paper/sag-vit-a-scale-aware-high-fidelity-patching","metrics":{"F1":"98.61"},"code_links":[{"title":"shravan-18/SAG-ViT","url":"https://github.com/shravan-18/SAG-ViT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/sag-vit-a-scale-aware-high-fidelity-patching","title":"SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers","date":"2024-11-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/resnet-strikes-back-an-improved-training","title":"ResNet strikes back: An improved training procedure in timm","date":"2021-10-01","rows_on_this_dataset":1,"code_links":14,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/regnet-self-regulated-network-for-image","title":"RegNet: Self-Regulated Network for Image Classification","date":"2021-01-03","rows_on_this_dataset":1,"code_links":14,"syntology":null},{"paper":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","rows_on_this_dataset":1,"code_links":144,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":302,"samples_ran":171,"samples_unverified":131,"pointer_only_for_licence":112,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/res2net-a-new-multi-scale-backbone","title":"Res2Net: A New Multi-scale Backbone Architecture","date":"2019-04-02","rows_on_this_dataset":1,"code_links":34,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":3,"samples_unverified":6,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","rows_on_this_dataset":1,"code_links":146,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":71,"samples_ran":18,"samples_unverified":53,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","rows_on_this_dataset":2,"code_links":484,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":377,"samples_ran":230,"samples_unverified":147,"pointer_only_for_licence":187,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":762,"samples_ran":422,"samples_unverified":340,"pointer_only_for_licence":315,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}