{"url":"/dataset/pascal-voc-2007","name":"PASCAL VOC 2007","full_name":"PASCAL VOC 2007","description_markdown":"**PASCAL VOC 2007** is a dataset for image recognition. The twenty object classes that have been selected are:\r\n\r\nPerson: person\r\nAnimal: bird, cat, cow, dog, horse, sheep\r\nVehicle: aeroplane, bicycle, boat, bus, car, motorbike, train\r\nIndoor: bottle, chair, dining table, potted plant, sofa, tv/monitor\r\n\r\nThe dataset can be used for image classification and object detection tasks.\r\n\r\nImage Source: [Object Detection and Recognition in Images](https://arxiv.org/abs/1708.01241)","description_withheld":null,"homepage":"http://host.robots.ox.ac.uk/pascal/VOC/voc2007/","introduced_date":null,"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":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Multi-Label Classification","url":"/task/multi-label-classification","datasets_with_task":"/datasets/task/multi-label-classification"},{"name":"Object Localization","url":"/task/object-localization","datasets_with_task":"/datasets/task/object-localization"},{"name":"Cross-Modal Retrieval","url":"/task/cross-modal-retrieval","datasets_with_task":"/datasets/task/cross-modal-retrieval"},{"name":"Weakly Supervised Object Detection","url":"/task/weakly-supervised-object-detection","datasets_with_task":"/datasets/task/weakly-supervised-object-detection"},{"name":"Object Counting","url":"/task/object-counting","datasets_with_task":"/datasets/task/object-counting"},{"name":"Unsupervised Semantic Segmentation with Language-image Pre-training","url":"/task/unsupervised-semantic-segmentation-with","datasets_with_task":"/datasets/task/unsupervised-semantic-segmentation-with"},{"name":"Unsupervised Object Detection","url":"/task/unsupervised-object-detection","datasets_with_task":"/datasets/task/unsupervised-object-detection"},{"name":"Real-Time Object Detection","url":"/task/real-time-object-detection","datasets_with_task":"/datasets/task/real-time-object-detection"},{"name":"Zero-Shot Object Detection","url":"/task/zero-shot-object-detection","datasets_with_task":"/datasets/task/zero-shot-object-detection"},{"name":"Open World Object Detection","url":"/task/open-world-object-detection","datasets_with_task":"/datasets/task/open-world-object-detection"},{"name":"Robust Object Detection","url":"/task/robust-object-detection","datasets_with_task":"/datasets/task/robust-object-detection"},{"name":"Unsupervised Object Localization","url":"/task/unsupervised-object-localization","datasets_with_task":"/datasets/task/unsupervised-object-localization"},{"name":"Multi-label Image Recognition with Partial Labels","url":"/task/multi-label-image-recognition-with-partial","datasets_with_task":"/datasets/task/multi-label-image-recognition-with-partial"}],"languages":[],"variants":["PASCAL VOC 2007","Pascal VOC 2007 count-test"],"data_loaders":[{"repo":"https://github.com/open-mmlab/mmdetection","url":"https://github.com/open-mmlab/mmdetection/blob/master/docs/1_exist_data_model.md","frameworks":["pytorch"]},{"repo":"https://github.com/open-mmlab/mmsegmentation","url":"https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md","frameworks":["pytorch"]},{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/pascal-voc-2007-dataset","frameworks":["tf","pytorch"]},{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/voc","frameworks":["tf","jax"]}],"num_papers_in_archive":126,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/weakly-supervised-object-detection-on-pascal-1","task":"Weakly Supervised Object Detection","dataset_variant":"PASCAL VOC 2007","rows":41,"metrics":["MAP"],"first_row_in_archive_order":{"model":"WeakSAM-MIST-DINO (with SAM)","paper":"/paper/weaksam-segment-anything-meets-weakly","metrics":{"MAP":"73.4"},"code_links":[{"title":"hustvl/weaksam","url":"https://github.com/hustvl/weaksam"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/object-detection-on-pascal-voc-2007","task":"Object Detection","dataset_variant":"PASCAL VOC 2007","rows":30,"metrics":["MAP","AP50","mAP@50","mAP@50-95","box AP"],"first_row_in_archive_order":{"model":"Cascade Eff-B7 NAS-FPN (Copy Paste pre-training, single-scale)","paper":"/paper/simple-copy-paste-is-a-strong-data","metrics":{"MAP":"89.3%"},"code_links":[{"title":"PaddlePaddle/PaddleOCR","url":"https://github.com/PaddlePaddle/PaddleOCR"},{"title":"open-mmlab/mmdetection","url":"https://github.com/open-mmlab/mmdetection"},{"title":"tensorflow/tpu","url":"https://github.com/tensorflow/tpu/tree/master/models/official/detection/projects/copy_paste"},{"title":"conradry/copy-paste-aug","url":"https://github.com/conradry/copy-paste-aug"},{"title":"RocketFlash/CAP_augmentation","url":"https://github.com/RocketFlash/CAP_augmentation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multi-label-classification-on-pascal-voc-2007","task":"Multi-Label Classification","dataset_variant":"PASCAL VOC 2007","rows":17,"metrics":["mAP"],"first_row_in_archive_order":{"model":"Q2L-CvT(ImageNet-21K pretrained, resolution 384)","paper":"/paper/query2label-a-simple-transformer-way-to-multi","metrics":{"mAP":"97.3"},"code_links":[{"title":"SlongLiu/query2labels","url":"https://github.com/SlongLiu/query2labels"},{"title":"curt-tigges/query2label","url":"https://github.com/curt-tigges/query2label"},{"title":"averyfallson/rmffn","url":"https://github.com/averyfallson/rmffn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/object-counting-on-pascal-voc-2007-count-test","task":"Object Counting","dataset_variant":"Pascal VOC 2007 count-test","rows":8,"metrics":["m-reIRMSE-nz","m-relRMSE","mRMSE","mRMSE-nz"],"first_row_in_archive_order":{"model":"Supervised Density Map","paper":"/paper/object-counting-and-instance-segmentation","metrics":{"m-reIRMSE-nz":"0.61","m-relRMSE":"0.17","mRMSE":"0.29","mRMSE-nz":"1.14"},"code_links":[{"title":"GuoleiSun/CountSeg","url":"https://github.com/GuoleiSun/CountSeg"},{"title":"alzayats/CountSeg-1","url":"https://github.com/alzayats/CountSeg-1"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multi-label-image-recognition-with-partial-1","task":"Multi-label Image Recognition with Partial Labels","dataset_variant":"PASCAL VOC 2007","rows":6,"metrics":["Average mAP"],"first_row_in_archive_order":{"model":"DualCoOp+TaI-DPT","paper":"/paper/texts-as-images-in-prompt-tuning-for-multi","metrics":{"Average mAP":"94.8"},"code_links":[{"title":"guozix/tai-dpt","url":"https://github.com/guozix/tai-dpt"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/real-time-object-detection-on-pascal-voc-2007","task":"Real-Time Object Detection","dataset_variant":"PASCAL VOC 2007","rows":4,"metrics":["FPS","MAP"],"first_row_in_archive_order":{"model":"YOLO","paper":"/paper/you-only-look-once-unified-real-time-object","metrics":{"FPS":"46.0","MAP":"63.4%"},"code_links":[{"title":"AlexeyAB/darknet","url":"https://github.com/AlexeyAB/darknet"},{"title":"PaddlePaddle/PaddleDetection","url":"https://github.com/PaddlePaddle/PaddleDetection"},{"title":"thtrieu/darkflow","url":"https://github.com/thtrieu/darkflow"},{"title":"PaddlePaddle/PaddleClas","url":"https://github.com/PaddlePaddle/PaddleClas"},{"title":"leggedrobotics/darknet_ros","url":"https://github.com/leggedrobotics/darknet_ros"},{"title":"Guanghan/ROLO","url":"https://github.com/Guanghan/ROLO"},{"title":"natanielruiz/android-yolo","url":"https://github.com/natanielruiz/android-yolo"},{"title":"JunshengFu/vehicle-detection","url":"https://github.com/JunshengFu/vehicle-detection"},{"title":"yeahkun/caffe-yolo","url":"https://github.com/yeahkun/caffe-yolo"},{"title":"Sharpiless/yolov3-vehicle-detection-paddle","url":"https://github.com/Sharpiless/yolov3-vehicle-detection-paddle"},{"title":"makatx/YOLO_ResNet","url":"https://github.com/makatx/YOLO_ResNet"},{"title":"westerndigitalcorporation/YOLOv3-in-PyTorch","url":"https://github.com/westerndigitalcorporation/YOLOv3-in-PyTorch"},{"title":"leon-liangwu/MaskYolo_Caffe","url":"https://github.com/leon-liangwu/MaskYolo_Caffe"},{"title":"subodh-malgonde/vehicle-detection","url":"https://github.com/subodh-malgonde/vehicle-detection"},{"title":"tanjeffreyz/yolo-v1","url":"https://github.com/tanjeffreyz/yolo-v1"},{"title":"vvasilo/yolov3_pytorch_ros","url":"https://github.com/vvasilo/yolov3_pytorch_ros"},{"title":"BathVisArtData/PeopleArt","url":"https://github.com/BathVisArtData/PeopleArt"},{"title":"saimj7/Social-Distancing-Detection-in-Real-Time","url":"https://github.com/saimj7/Social-Distancing-Detection-in-Real-Time"},{"title":"srihari-humbarwadi/YOLOv1-TensorFlow2.0","url":"https://github.com/srihari-humbarwadi/YOLOv1-TensorFlow2.0"},{"title":"patrick013/object-detection---yolov3","url":"https://github.com/patrick013/object-detection---yolov3"},{"title":"damien911224/theWorldInSafety","url":"https://github.com/damien911224/theWorldInSafety"},{"title":"km1414/CNN-models","url":"https://github.com/km1414/CNN-models"},{"title":"rocapal/fish_detection","url":"https://github.com/rocapal/fish_detection"},{"title":"kirilcvetkov92/Vehicle-Detection","url":"https://github.com/kirilcvetkov92/Vehicle-Detection"},{"title":"Qengineering/YoloV3-ncnn-Raspberry-Pi-4","url":"https://github.com/Qengineering/YoloV3-ncnn-Raspberry-Pi-4"},{"title":"Qengineering/MobileNetV2_YOLOV3_ncnn","url":"https://github.com/Qengineering/MobileNetV2_YOLOV3_ncnn"},{"title":"SHU-FLYMAN/Yolov1-TensorFlow","url":"https://github.com/SHU-FLYMAN/Yolov1-TensorFlow"},{"title":"jshaffer94247/Counting-Fish","url":"https://github.com/jshaffer94247/Counting-Fish"},{"title":"ivanwhaf/yolov1-pytorch","url":"https://github.com/ivanwhaf/yolov1-pytorch"},{"title":"tossy0423/darknet_ros","url":"https://github.com/tossy0423/darknet_ros"},{"title":"yakhyo/YOLOv1-pt","url":"https://github.com/yakhyo/YOLOv1-pt"},{"title":"shkim960520/YOLO-v1-for-studying","url":"https://github.com/shkim960520/YOLO-v1-for-studying"},{"title":"zjZSTU/YOLO_v1","url":"https://github.com/zjZSTU/YOLO_v1"},{"title":"MINED30/Face_Mask_Detection_YOLO","url":"https://github.com/MINED30/Face_Mask_Detection_YOLO"},{"title":"DevBruce/YOLOv1-TF2","url":"https://github.com/DevBruce/YOLOv1-TF2"},{"title":"TheClub4/car-detection-yolov2","url":"https://github.com/TheClub4/car-detection-yolov2"},{"title":"DevBruce/YOLO-TF2","url":"https://github.com/DevBruce/YOLO-TF2"},{"title":"Banus/caffe-demo","url":"https://github.com/Banus/caffe-demo"},{"title":"michhar/azureml-keras-yolov3-custom","url":"https://github.com/michhar/azureml-keras-yolov3-custom"},{"title":"leon-liangwu/py-caffe-yolo","url":"https://github.com/leon-liangwu/py-caffe-yolo"},{"title":"ssaru/You_Only_Look_Once","url":"https://github.com/ssaru/You_Only_Look_Once"},{"title":"OFRIN/Tensorflow_YOLOv1","url":"https://github.com/OFRIN/Tensorflow_YOLOv1"},{"title":"samson6460/tf2_YOLO","url":"https://github.com/samson6460/tf2_YOLO"},{"title":"hamidriasat/-Computer-Vision-and-Deep-Learning","url":"https://github.com/hamidriasat/-Computer-Vision-and-Deep-Learning"},{"title":"hamidriasat/Computer-Vision-and-Deep-Learning","url":"https://github.com/hamidriasat/Computer-Vision-and-Deep-Learning"},{"title":"sprenkle/VectorCards","url":"https://github.com/sprenkle/VectorCards"},{"title":"Stepan-coder/HandWritenSignatureDetection","url":"https://github.com/Stepan-coder/HandWritenSignatureDetection"},{"title":"Kartik-Aggarwal/Real-Time-Traffic-Sign-Detection","url":"https://github.com/Kartik-Aggarwal/Real-Time-Traffic-Sign-Detection"},{"title":"Deeplodocus/COCO-with-YOLO","url":"https://github.com/Deeplodocus/COCO-with-YOLO"},{"title":"anishreddy3/Autonomous-driving-application-car-detection","url":"https://github.com/anishreddy3/Autonomous-driving-application-car-detection"},{"title":"zer0sh0t/artificial_intelligence","url":"https://github.com/zer0sh0t/artificial_intelligence/tree/master/object_detection/you_only_look_once"},{"title":"abajaj945/Ship-Detection-using-Tensorflow","url":"https://github.com/abajaj945/Ship-Detection-using-Tensorflow"},{"title":"nsoul97/yolov1_pytorch","url":"https://github.com/nsoul97/yolov1_pytorch"},{"title":"youssef-kishk/ROS-Based-Robot-Object-Detection-Recognition","url":"https://github.com/youssef-kishk/ROS-Based-Robot-Object-Detection-Recognition"},{"title":"RobbertBrand/Yolo-Tensorflow-Implementation","url":"https://github.com/RobbertBrand/Yolo-Tensorflow-Implementation"},{"title":"GiaKhangLuu/YOLOv1_from_scratch","url":"https://github.com/GiaKhangLuu/YOLOv1_from_scratch"},{"title":"Vijayabhaskar96/Object-Detection-Algorithms","url":"https://github.com/Vijayabhaskar96/Object-Detection-Algorithms"},{"title":"BonJunKu/ScooterHelmetDetector","url":"https://github.com/BonJunKu/ScooterHelmetDetector"},{"title":"DavianYang/yolo.ai","url":"https://github.com/DavianYang/yolo.ai"},{"title":"jalotra/Queue-Detection","url":"https://github.com/jalotra/Queue-Detection"},{"title":"solaris33/YOLO-v1-tf2","url":"https://github.com/solaris33/YOLO-v1-tf2"},{"title":"jalotra/Queue-Detection-","url":"https://github.com/jalotra/Queue-Detection-"},{"title":"aurelien-peden/Deep-Learning-paper-implementations","url":"https://github.com/aurelien-peden/Deep-Learning-paper-implementations"},{"title":"3epochs/you-only-look-once","url":"https://github.com/3epochs/you-only-look-once"},{"title":"Vonski/wdi19","url":"https://github.com/Vonski/wdi19"},{"title":"Ereebay/Deep-Learning-Documents","url":"https://github.com/Ereebay/Deep-Learning-Documents"},{"title":"Ereebay/DeepLearningDocuments","url":"https://github.com/Ereebay/DeepLearningDocuments"},{"title":"eric-erki/android-yolo","url":"https://github.com/eric-erki/android-yolo"},{"title":"1991yuyang/YOLOV1-PYTORCH","url":"https://github.com/1991yuyang/YOLOV1-PYTORCH"},{"title":"Ugenteraan/YOLOv1","url":"https://github.com/Ugenteraan/YOLOv1"},{"title":"Ereebay/CV-Documents","url":"https://github.com/Ereebay/CV-Documents"},{"title":"sunlizhuang/YOLOv1-PaddlePaddle","url":"https://github.com/sunlizhuang/YOLOv1-PaddlePaddle"},{"title":"ankitAMD/Darkflow-object-detection","url":"https://github.com/ankitAMD/Darkflow-object-detection"},{"title":"msuhail1997/YOLO-Pytorch","url":"https://github.com/msuhail1997/YOLO-Pytorch"},{"title":"usnistgov/object-detection-yolov3","url":"https://github.com/usnistgov/object-detection-yolov3"},{"title":"Nikhil619/Autonomous-driving-with-camera-using-Yolo","url":"https://github.com/Nikhil619/Autonomous-driving-with-camera-using-Yolo"},{"title":"ridhaalkhabaz/YTVidObjectDectector","url":"https://github.com/ridhaalkhabaz/YTVidObjectDectector"},{"title":"msuhail1997/YOLOv1-Pytorch","url":"https://github.com/msuhail1997/YOLOv1-Pytorch"},{"title":"ngrayluna/NotCake","url":"https://github.com/ngrayluna/NotCake"},{"title":"msuhail1997/YOLO-Pytorch-Object_Detection","url":"https://github.com/msuhail1997/YOLO-Pytorch-Object_Detection"},{"title":"capstone-w3/trash_parent_repo","url":"https://github.com/capstone-w3/trash_parent_repo"},{"title":"vasukumar92/Autonomous-driving---Car-detection","url":"https://github.com/vasukumar92/Autonomous-driving---Car-detection"},{"title":"stephenharris/yolo-walkthrough","url":"https://github.com/stephenharris/yolo-walkthrough"},{"title":"euske/derpyolo","url":"https://github.com/euske/derpyolo"},{"title":"euske/miniyolo","url":"https://github.com/euske/miniyolo"},{"title":"danijorgesantos/detectcars","url":"https://github.com/danijorgesantos/detectcars"},{"title":"s1r2ikanth/Atunomous-Driving-Application--For-Car-detection","url":"https://github.com/s1r2ikanth/Atunomous-Driving-Application--For-Car-detection"},{"title":"ZUCCBBQ/yoloV3-","url":"https://github.com/ZUCCBBQ/yoloV3-"},{"title":"MasoumehVahedi/Yolo-Object-Detection","url":"https://github.com/MasoumehVahedi/Yolo-Object-Detection"},{"title":"noelcodes/YOLO","url":"https://github.com/noelcodes/YOLO"},{"title":"chuongngd/Images-Object-Detection","url":"https://github.com/chuongngd/Images-Object-Detection"},{"title":"roya90/android-yolo","url":"https://github.com/roya90/android-yolo"},{"title":"you-leee/deep-nn-examples","url":"https://github.com/you-leee/deep-nn-examples"},{"title":"gunooknam/Code_Study_Yolov3","url":"https://github.com/gunooknam/Code_Study_Yolov3"},{"title":"stevenzhou2017/MaskYolo","url":"https://github.com/stevenzhou2017/MaskYolo"},{"title":"JitindraFartiyal/Object-Detection","url":"https://github.com/JitindraFartiyal/Object-Detection"},{"title":"Singh-sid930/YOLO_pytorch","url":"https://github.com/Singh-sid930/YOLO_pytorch"},{"title":"softbankrobotics-research/darknet_ros","url":"https://github.com/softbankrobotics-research/darknet_ros"},{"title":"taranjotsingh01/Car-detection-using-YOLO","url":"https://github.com/taranjotsingh01/Car-detection-using-YOLO"},{"title":"dongkyuk0419/YOLO","url":"https://github.com/dongkyuk0419/YOLO"},{"title":"qwer10/CarDetection","url":"https://github.com/qwer10/CarDetection"},{"title":"GianRomani/YOLOv3-Neural-Networks-project-","url":"https://github.com/GianRomani/YOLOv3-Neural-Networks-project-"},{"title":"Bharat-mtr/yolov1-tf2","url":"https://dagshub.com/Bharat-mtr/yolov1-tf2"},{"title":"saurabh241930/yolo_v2_tensorflow","url":"https://github.com/saurabh241930/yolo_v2_tensorflow"},{"title":"issaiass/FacialMaskDetector","url":"https://github.com/issaiass/FacialMaskDetector"},{"title":"SiHaoShen/Convolutional-Neural-Networks","url":"https://github.com/SiHaoShen/Convolutional-Neural-Networks"},{"title":"MohamedAbd0/darkflow-training","url":"https://github.com/MohamedAbd0/darkflow-training"},{"title":"eunseo1092/Object-Detection","url":"https://github.com/eunseo1092/Object-Detection"},{"title":"Poseidon0711/Autonomous-driving-application---Car-detection","url":"https://github.com/Poseidon0711/Autonomous-driving-application---Car-detection"},{"title":"evarsha/YOLO-Object-Detection","url":"https://github.com/evarsha/YOLO-Object-Detection"},{"title":"gamilton211/Object-detection-YOLO","url":"https://github.com/gamilton211/Object-detection-YOLO"},{"title":"dyadav4/Autonomous-driving-application-car-detection","url":"https://github.com/dyadav4/Autonomous-driving-application-car-detection"},{"title":"Rohed/ml-1","url":"https://github.com/Rohed/ml-1"},{"title":"Donvink/Car_Detection_for_Autonomous_Driving","url":"https://github.com/Donvink/Car_Detection_for_Autonomous_Driving"},{"title":"Deep-Learner/Plot_CNNs_Python_Latex","url":"https://github.com/Deep-Learner/Plot_CNNs_Python_Latex"},{"title":"jiama843/tf-YOLO-pascalVOC","url":"https://github.com/jiama843/tf-YOLO-pascalVOC"},{"title":"Everina/car-detection-yolo","url":"https://github.com/Everina/car-detection-yolo"},{"title":"rudraina/Face-Verification-And-Validation","url":"https://github.com/rudraina/Face-Verification-And-Validation"},{"title":"keshav47/Face-Recognition-And-Verification","url":"https://github.com/keshav47/Face-Recognition-And-Verification"},{"title":"aldipiroli/yolo-v1","url":"https://github.com/aldipiroli/yolo-v1"},{"title":"theinmate4587/Autonomous-driving---Car-detection","url":"https://github.com/theinmate4587/Autonomous-driving---Car-detection"},{"title":"zhangxiutao/ROLO","url":"https://github.com/zhangxiutao/ROLO"},{"title":"Nandu960/Road-Asset-Detection","url":"https://github.com/Nandu960/Road-Asset-Detection"},{"title":"manankshastri/Object-Detection","url":"https://github.com/manankshastri/Object-Detection"},{"title":"WaelOuni/MergeTenserFlowWithOdb","url":"https://github.com/WaelOuni/MergeTenserFlowWithOdb"},{"title":"CUAI-CAU/Computer_vision_team_1","url":"https://github.com/CUAI-CAU/Computer_vision_team_1"},{"title":"poojabhore12/SocialDistancing-Detector","url":"https://github.com/poojabhore12/SocialDistancing-Detector"},{"title":"altomator/Introduction_to_Deep_Learning-2-Face_Detection","url":"https://github.com/altomator/Introduction_to_Deep_Learning-2-Face_Detection"},{"title":"JennEYoon/Coursera-DLAI","url":"https://github.com/JennEYoon/Coursera-DLAI"},{"title":"uchile-robotics-forks/darknet_ros","url":"https://github.com/uchile-robotics-forks/darknet_ros"},{"title":"ibrahiemhss/android-yolo","url":"https://github.com/ibrahiemhss/android-yolo"},{"title":"kiranrgupta26/ObjectDetection","url":"https://github.com/kiranrgupta26/ObjectDetection"},{"title":"alia21/VechicleDetection-and-Traccking","url":"https://github.com/alia21/VechicleDetection-and-Traccking"},{"title":"Abdulrahman-Adel/Object-Detection","url":"https://github.com/Abdulrahman-Adel/Object-Detection"},{"title":"bian0505/Pad_Me","url":"https://github.com/bian0505/Pad_Me"},{"title":"easternRainy/MSDS-DL-Final-YOLO-Implement","url":"https://github.com/easternRainy/MSDS-DL-Final-YOLO-Implement"},{"title":"YCHung1998/Object-Detection","url":"https://github.com/YCHung1998/Object-Detection"},{"title":"rishikc137/social-distancing-detector","url":"https://github.com/rishikc137/social-distancing-detector"},{"title":"ritesh2448/Text-Detection-And-Recognition","url":"https://github.com/ritesh2448/Text-Detection-And-Recognition"},{"title":"swaubhik/social-distance-detector","url":"https://github.com/swaubhik/social-distance-detector"},{"title":"gary-kaitung/data-science-portfolio","url":"https://github.com/gary-kaitung/data-science-portfolio"},{"title":"jiama843/tf-mnist","url":"https://github.com/jiama843/tf-mnist"},{"title":"chihyanghsu0805/object_detection_yolo","url":"https://github.com/chihyanghsu0805/object_detection_yolo"},{"title":"dvndra/car_detection_yolo","url":"https://github.com/dvndra/car_detection_yolo"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/open-world-object-detection-on-pascal-voc","task":"Open World Object Detection","dataset_variant":"PASCAL VOC 2007","rows":2,"metrics":["Unknown Recall","MAP","WI","A-OSE"],"first_row_in_archive_order":{"model":"ORE (MDef-DETR)","paper":"/paper/multi-modal-transformers-excel-at-class","metrics":{"A-OSE":"7322","MAP":"64.03","Unknown Recall":"50.13","WI":"0.0474"},"code_links":[{"title":"mmaaz60/mvits_for_class_agnostic_od","url":"https://github.com/mmaaz60/mvits_for_class_agnostic_od"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/robust-object-detection-on-pascal-voc-2007","task":"Robust Object Detection","dataset_variant":"PASCAL VOC 2007","rows":2,"metrics":["mPC [AP50]","rPC [%]"],"first_row_in_archive_order":{"model":"Faster R-CNN with Stylized Training Data","paper":"/paper/benchmarking-robustness-in-object-detection","metrics":{"mPC [AP50]":"56.2","rPC [%]":"69.9"},"code_links":[{"title":"bethgelab/imagecorruptions","url":"https://github.com/bethgelab/imagecorruptions"},{"title":"bethgelab/robust-detection-benchmark","url":"https://github.com/bethgelab/robust-detection-benchmark"},{"title":"bethgelab/stylize-datasets","url":"https://github.com/bethgelab/stylize-datasets"},{"title":"bethgelab/mmdetection","url":"https://github.com/bethgelab/mmdetection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/semantic-segmentation-on-pascal-voc-2007","task":"Semantic Segmentation","dataset_variant":"PASCAL VOC 2007","rows":2,"metrics":["Mean IoU"],"first_row_in_archive_order":{"model":"GALDNet","paper":"/paper/global-aggregation-then-local-distribution-in","metrics":{"Mean IoU":"83"},"code_links":[{"title":"lxtGH/GALD-DGCNet","url":"https://github.com/lxtGH/GALD-DGCNet"},{"title":"lxtGH/GALD-Net","url":"https://github.com/lxtGH/GALD-Net"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-classification-on-pascal-voc-2007","task":"Image Classification","dataset_variant":"PASCAL VOC 2007","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"NNCLR","paper":"/paper/with-a-little-help-from-my-friends-nearest","metrics":{"Accuracy":"83"},"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"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/object-localization-on-pascal-voc-2007","task":"Object Localization","dataset_variant":"PASCAL VOC 2007","rows":1,"metrics":["CorLoc"],"first_row_in_archive_order":{"model":"ours","paper":"/paper/co-localization-with-category-consistent-cnn","metrics":{"CorLoc":"41.2"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unsupervised-object-localization-on-pascal","task":"Unsupervised Object Localization","dataset_variant":"PASCAL VOC 2007","rows":1,"metrics":["CorLoc"],"first_row_in_archive_order":{"model":"DeepCut","paper":"/paper/deepcut-unsupervised-segmentation-using-graph","metrics":{"CorLoc":"69.8"},"code_links":[{"title":"sampl-weizmann/deepcut","url":"https://github.com/sampl-weizmann/deepcut"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unsupervised-semantic-segmentation-with-6","task":"Unsupervised Semantic Segmentation with Language-image Pre-training","dataset_variant":"PASCAL VOC 2007","rows":1,"metrics":["Mean IoU (val)"],"first_row_in_archive_order":{"model":"CLIPpy ViT-B","paper":"/paper/perceptual-grouping-in-vision-language-models","metrics":{"Mean IoU (val)":"52.2"},"code_links":[{"title":"kahnchana/clippy","url":"https://github.com/kahnchana/clippy"},{"title":"jongwoopark7978/LVNet","url":"https://github.com/jongwoopark7978/LVNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/omnicount-multi-label-object-counting-with","title":"OmniCount: Multi-label Object Counting with Semantic-Geometric Priors","date":"2024-03-08","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/weaksam-segment-anything-meets-weakly","title":"WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition","date":"2024-02-22","rows_on_this_dataset":6,"code_links":1,"syntology":null},{"paper":"/paper/yolo-former-yolo-shakes-hand-with-vit","title":"YOLO-Former: YOLO Shakes Hand With ViT","date":"2024-01-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/inner-iou-more-effective-intersection-over","title":"Inner-IoU: More Effective Intersection over Union Loss with Auxiliary Bounding Box","date":"2023-11-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ultra-efficient-on-device-object-detection-on","title":"Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO","date":"2023-11-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/gkgnet-group-k-nearest-neighbor-based-graph","title":"GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition","date":"2023-08-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/femtodet-an-object-detection-baseline-for","title":"FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs","date":"2023-01-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deepcut-unsupervised-segmentation-using-graph","title":"DeepCut: Unsupervised Segmentation using Graph Neural Networks Clustering","date":"2022-12-12","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":4,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/texts-as-images-in-prompt-tuning-for-multi","title":"Texts as Images in Prompt Tuning for Multi-Label Image Recognition","date":"2022-11-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/perceptual-grouping-in-vision-language-models","title":"Perceptual Grouping in Contrastive Vision-Language Models","date":"2022-10-18","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/dpnet-dual-path-network-for-real-time-object","title":"DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention","date":"2022-09-28","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/combining-metric-learning-and-attention-heads","title":"Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification","date":"2022-09-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/object-discovery-via-contrastive-learning-for","title":"Object Discovery via Contrastive Learning for Weakly Supervised Object Detection","date":"2022-08-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dualcoop-fast-adaptation-to-multi-label","title":"DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations","date":"2022-06-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/semantic-aware-representation-blending-for-1","title":"Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels","date":"2022-05-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/heterogeneous-semantic-transfer-for-multi","title":"Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels","date":"2022-05-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/semantic-aware-representation-blending-for","title":"Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels","date":"2022-03-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":6,"samples_unverified":1,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/structured-semantic-transfer-for-multi-label","title":"Structured Semantic Transfer for Multi-Label Recognition with Partial Labels","date":"2021-12-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multi-modal-transformers-excel-at-class","title":"Class-agnostic Object Detection with Multi-modal Transformer","date":"2021-11-22","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/transformer-based-dual-relation-graph-for-1","title":"Transformer-based Dual Relation Graph for Multi-label Image Recognition","date":"2021-10-10","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":9,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/m3tr-multi-modal-multi-label-recognition-with","title":"M3TR: Multi-modal Multi-label Recognition with Transformer","date":"2021-10-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/eeea-net-an-early-exit-evolutionary-neural","title":"EEEA-Net: An Early Exit Evolutionary Neural Architecture Search","date":"2021-08-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/query2label-a-simple-transformer-way-to-multi","title":"Query2Label: A Simple Transformer Way to Multi-Label Classification","date":"2021-07-22","rows_on_this_dataset":3,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multi-layered-semantic-representation-network","title":"Multi-layered Semantic Representation Network for Multi-label Image Classification","date":"2021-06-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/with-a-little-help-from-my-friends-nearest","title":"With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations","date":"2021-04-29","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/imagenet-21k-pretraining-for-the-masses","title":"ImageNet-21K Pretraining for the Masses","date":"2021-04-22","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/towards-open-world-object-detection","title":"Towards Open World Object Detection","date":"2021-03-03","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/simple-copy-paste-is-a-strong-data","title":"Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation","date":"2020-12-13","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-visual-representations-for-transfer-1","title":"Learning Visual Representations for Transfer Learning by Suppressing Texture","date":"2020-11-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/comprehensive-attention-self-distillation-for","title":"Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection","date":"2020-10-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":4,"samples_unverified":2,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/towards-automatic-visual-inspection-a-weakly","title":"Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection","date":"2020-10-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/localize-to-classify-and-classify-to-localize","title":"Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection","date":"2020-09-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/asymmetric-loss-for-multi-label","title":"Asymmetric Loss For Multi-Label Classification","date":"2020-09-29","rows_on_this_dataset":2,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":4,"samples_unverified":8,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multi-label-image-recognition-with-multi","title":"Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition","date":"2020-07-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/self-knowledge-distillation-a-simple-way-for","title":"Self-Knowledge Distillation with Progressive Refinement of Targets","date":"2020-06-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/instance-aware-context-focused-and-memory","title":"Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection","date":"2020-04-09","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/object-instance-mining-for-weakly-supervised","title":"Object Instance Mining for Weakly Supervised Object Detection","date":"2020-02-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/training-object-detectors-from-few-weakly-1","title":"Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images","date":"2019-12-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/towards-precise-end-to-end-weakly-supervised-1","title":"Towards Precise End-to-end Weakly Supervised Object Detection Network","date":"2019-11-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/wsod-with-psnet-and-box-regression","title":"WSOD with PSNet and Box Regression","date":"2019-11-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/adaptively-denoising-proposal-collection","title":"Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization","date":"2019-10-04","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/hierarchical-shot-detector","title":"Hierarchical Shot Detector","date":"2019-10-01","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/c-midn-coupled-multiple-instance-detection","title":"C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection","date":"2019-10-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/global-aggregation-then-local-distribution-in","title":"Global Aggregation then Local Distribution in Fully Convolutional Networks","date":"2019-09-16","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/wsod2-learning-bottom-up-and-top-down","title":"WSOD2: Learning Bottom-up and Top-down Objectness Distillation forWeakly-supervised Object Detection","date":"2019-09-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/learning-semantic-specific-graph","title":"Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition","date":"2019-08-20","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/object-aware-instance-labeling-for-weakly","title":"Object-Aware Instance Labeling for Weakly Supervised Object Detection","date":"2019-08-10","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/benchmarking-robustness-in-object-detection","title":"Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming","date":"2019-07-17","rows_on_this_dataset":2,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":2,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/utilizing-the-instability-in-weakly","title":"Utilizing the Instability in Weakly Supervised Object Detection","date":"2019-06-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/you-reap-what-you-sow-using-videos-to","title":"You Reap What You Sow: Using Videos to Generate High Precision Object Proposals for Weakly-Supervised Object Detection","date":"2019-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/cyclic-guidance-for-weakly-supervised-joint","title":"Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation","date":"2019-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/objects-as-points","title":"Objects as Points","date":"2019-04-16","rows_on_this_dataset":1,"code_links":76,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":130,"samples_ran":10,"samples_unverified":120,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/c-mil-continuation-multiple-instance-learning","title":"C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection","date":"2019-04-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multi-label-image-recognition-with-graph","title":"Multi-Label Image Recognition with Graph Convolutional Networks","date":"2019-04-07","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/thundernet-towards-real-time-generic-object","title":"ThunderNet: Towards Real-time Generic Object Detection","date":"2019-03-28","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/object-counting-and-instance-segmentation","title":"Object Counting and Instance Segmentation with Image-level Supervision","date":"2019-03-06","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/min-entropy-latent-model-for-weakly","title":"Min-Entropy Latent Model for Weakly Supervised Object Detection","date":"2019-02-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dissimilarity-coefficient-based-weakly","title":"Dissimilarity Coefficient based Weakly Supervised Object Detection","date":"2018-11-25","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/softer-nms-rethinking-bounding-box-regression","title":"Bounding Box Regression with Uncertainty for Accurate Object Detection","date":"2018-09-23","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fewer-is-more-image-segmentation-based-weakly","title":"Fewer is More: Image Segmentation Based Weakly Supervised Object Detection with Partial Aggregation","date":"2018-09-04","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/where-are-the-blobs-counting-by-localization","title":"Where are the Blobs: Counting by Localization with Point Supervision","date":"2018-07-25","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":0,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pcl-proposal-cluster-learning-for-weakly","title":"PCL: Proposal Cluster Learning for Weakly Supervised Object Detection","date":"2018-07-09","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":1,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/w2f-a-weakly-supervised-to-fully-supervised","title":"W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection","date":"2018-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/zigzag-learning-for-weakly-supervised-object","title":"Zigzag Learning for Weakly Supervised Object Detection","date":"2018-04-25","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/multi-evidence-filtering-and-fusion-for-multi","title":"Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning","date":"2018-02-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/random-erasing-data-augmentation","title":"Random Erasing Data Augmentation","date":"2017-08-16","rows_on_this_dataset":1,"code_links":18,"syntology":null},{"paper":"/paper/couplenet-coupling-global-structure-with","title":"CoupleNet: Coupling Global Structure with Local Parts for Object Detection","date":"2017-08-09","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/blitznet-a-real-time-deep-network-for-scene","title":"BlitzNet: A Real-Time Deep Network for Scene Understanding","date":"2017-08-09","rows_on_this_dataset":3,"code_links":2,"syntology":null},{"paper":"/paper/exploiting-web-images-for-weakly-supervised","title":"Exploiting Web Images for Weakly Supervised Object Detection","date":"2017-07-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/revisiting-unreasonable-effectiveness-of-data","title":"Revisiting Unreasonable Effectiveness of Data in Deep Learning Era","date":"2017-07-10","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/variational-bayesian-multiple-instance","title":"Variational Bayesian Multiple Instance Learning With Gaussian Processes","date":"2017-07-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/few-example-object-detection-with-model","title":"Few-Example Object Detection with Model Communication","date":"2017-06-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-self-taught-learning-for-weakly","title":"Deep Self-Taught Learning for Weakly Supervised Object Localization","date":"2017-04-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-fast-rcnn-hard-positive-generation-via","title":"A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection","date":"2017-04-11","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/multiple-instance-detection-network-with","title":"Multiple Instance Detection Network with Online Instance Classifier Refinement","date":"2017-04-01","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/denet-scalable-real-time-object-detection","title":"DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling","date":"2017-03-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/bridging-saliency-detection-to-weakly","title":"Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning","date":"2017-03-03","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/yolo9000-better-faster-stronger","title":"YOLO9000: Better, Faster, Stronger","date":"2016-12-25","rows_on_this_dataset":1,"code_links":231,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":60,"samples_ran":16,"samples_unverified":44,"pointer_only_for_licence":22,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/co-localization-with-category-consistent-cnn","title":"Co-localization with Category-Consistent Features and Geodesic Distance Propagation","date":"2016-12-10","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/weakly-supervised-cascaded-convolutional","title":"Weakly Supervised Cascaded Convolutional Networks","date":"2016-11-24","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-label-distribution-learning-with-label","title":"Deep Label Distribution Learning with Label Ambiguity","date":"2016-11-06","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/contextlocnet-context-aware-deep-network","title":"ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization","date":"2016-09-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/self-paced-deep-learning-for-weakly","title":"Self Paced Deep Learning for Weakly Supervised Object Detection","date":"2016-05-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/r-fcn-object-detection-via-region-based-fully","title":"R-FCN: Object Detection via Region-based Fully Convolutional Networks","date":"2016-05-20","rows_on_this_dataset":1,"code_links":48,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":1,"samples_unverified":10,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/subcategory-aware-convolutional-neural","title":"Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection","date":"2016-04-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/training-region-based-object-detectors-with","title":"Training Region-based Object Detectors with Online Hard Example Mining","date":"2016-04-12","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/counting-everyday-objects-in-everyday-scenes","title":"Counting Everyday Objects in Everyday Scenes","date":"2016-04-12","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/ssd-single-shot-multibox-detector","title":"SSD: Single Shot MultiBox Detector","date":"2015-12-08","rows_on_this_dataset":1,"code_links":221,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":131,"samples_ran":19,"samples_unverified":112,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/weakly-supervised-deep-detection-networks","title":"Weakly Supervised Deep Detection Networks","date":"2015-11-09","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/you-only-look-once-unified-real-time-object","title":"You Only Look Once: Unified, Real-Time Object Detection","date":"2015-06-08","rows_on_this_dataset":2,"code_links":144,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":148,"samples_ran":80,"samples_unverified":68,"pointer_only_for_licence":98,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fast-r-cnn","title":"Fast R-CNN","date":"2015-04-30","rows_on_this_dataset":1,"code_links":30,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":5,"samples_unverified":3,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/exploit-bounding-box-annotations-for-multi","title":"Exploit Bounding Box Annotations for Multi-label Object Recognition","date":"2015-04-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deformable-part-models-are-convolutional","title":"Deformable Part Models are Convolutional Neural Networks","date":"2014-09-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/spatial-pyramid-pooling-in-deep-convolutional","title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","date":"2014-06-18","rows_on_this_dataset":1,"code_links":14,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/on-learning-to-localize-objects-with-minimal","title":"On learning to localize objects with minimal supervision","date":"2014-03-05","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/rich-feature-hierarchies-for-accurate-object","title":"Rich feature hierarchies for accurate object detection and semantic segmentation","date":"2013-11-11","rows_on_this_dataset":1,"code_links":29,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":1,"samples_unverified":16,"pointer_only_for_licence":0,"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":26,"samples_harvested":609,"samples_ran":181,"samples_unverified":428,"pointer_only_for_licence":151,"papers_with_no_sample_that_ran":4,"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."}