{"url":"/task/object","name":"Object","slug":"object","description_markdown":"Replace the cat with a British Shorthair cat of the breed with bulging yellow eyes","categories":[{"name":"Adversarial","url":"/area/adversarial"},{"name":"Audio","url":"/area/audio"},{"name":"Computer Code","url":"/area/computer-code"},{"name":"Computer Vision","url":"/area/computer-vision"},{"name":"Graphs","url":"/area/graphs"},{"name":"Knowledge Base","url":"/area/knowledge-base"},{"name":"Medical","url":"/area/medical"},{"name":"Miscellaneous","url":"/area/miscellaneous"},{"name":"Music","url":"/area/music"},{"name":"Playing Games","url":"/area/playing-games"},{"name":"Robots","url":"/area/robots"},{"name":"Time Series","url":"/area/time-series"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"derived"},"counts":{"papers_tagged":10696,"papers_with_code":3979,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"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":0,"subtasks":0,"parent_tasks":1},"benchmarks":[],"datasets":[],"subtasks":[],"parent_tasks":[{"url":"/task/10-shot-image-generation","name":"10-shot image generation"}],"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":3979,"tagged_in_all":10696,"items":[{"url":"/paper/focal-loss-for-dense-object-detection","title":"Focal Loss for Dense Object Detection","date":"2017-08-07","arxiv_id":"1708.02002","repositories_listed":234,"syntology":{"n":11,"n_ran":11,"n_unverified":0,"n_pointer_only":6}},{"url":"/paper/yolo9000-better-faster-stronger","title":"YOLO9000: Better, Faster, Stronger","date":"2016-12-25","arxiv_id":"1612.08242","repositories_listed":231,"syntology":{"n":60,"n_ran":16,"n_unverified":44,"n_pointer_only":22}},{"url":"/paper/yolov4-optimal-speed-and-accuracy-of-object","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","date":"2020-04-23","arxiv_id":"2004.10934","repositories_listed":223,"syntology":{"n":184,"n_ran":24,"n_unverified":160,"n_pointer_only":8}},{"url":"/paper/ssd-single-shot-multibox-detector","title":"SSD: Single Shot MultiBox Detector","date":"2015-12-08","arxiv_id":"1512.02325","repositories_listed":221,"syntology":{"n":131,"n_ran":19,"n_unverified":112,"n_pointer_only":5}},{"url":"/paper/faster-r-cnn-towards-real-time-object","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","date":"2015-06-04","arxiv_id":"1506.01497","repositories_listed":196,"syntology":{"n":124,"n_ran":59,"n_unverified":65,"n_pointer_only":42}},{"url":"/paper/mask-r-cnn","title":"Mask R-CNN","date":"2017-03-20","arxiv_id":"1703.06870","repositories_listed":179,"syntology":{"n":140,"n_ran":42,"n_unverified":98,"n_pointer_only":23}},{"url":"/paper/you-only-look-once-unified-real-time-object","title":"You Only Look Once: Unified, Real-Time Object Detection","date":"2015-06-08","arxiv_id":"1506.02640","repositories_listed":144,"syntology":{"n":148,"n_ran":80,"n_unverified":68,"n_pointer_only":98}},{"url":"/paper/fcos-fully-convolutional-one-stage-object","title":"FCOS: Fully Convolutional One-Stage Object Detection","date":"2019-04-02","arxiv_id":"1904.01355","repositories_listed":87,"syntology":{"n":40,"n_ran":13,"n_unverified":27,"n_pointer_only":18}},{"url":"/paper/feature-pyramid-networks-for-object-detection","title":"Feature Pyramid Networks for Object Detection","date":"2016-12-09","arxiv_id":"1612.03144","repositories_listed":85,"syntology":{"n":51,"n_ran":16,"n_unverified":35,"n_pointer_only":11}},{"url":"/paper/objects-as-points","title":"Objects as Points","date":"2019-04-16","arxiv_id":"1904.07850","repositories_listed":76,"syntology":{"n":130,"n_ran":10,"n_unverified":120,"n_pointer_only":0}},{"url":"/paper/frustum-pointnets-for-3d-object-detection","title":"Frustum PointNets for 3D Object Detection from RGB-D Data","date":"2017-11-22","arxiv_id":"1711.08488","repositories_listed":68,"syntology":{"n":4,"n_ran":2,"n_unverified":2,"n_pointer_only":2}},{"url":"/paper/efficientdet-scalable-and-efficient-object","title":"EfficientDet: Scalable and Efficient Object Detection","date":"2019-11-20","arxiv_id":"1911.09070","repositories_listed":64,"syntology":{"n":70,"n_ran":11,"n_unverified":59,"n_pointer_only":3}},{"url":"/paper/r-fcn-object-detection-via-region-based-fully","title":"R-FCN: Object Detection via Region-based Fully Convolutional Networks","date":"2016-05-20","arxiv_id":"1605.06409","repositories_listed":48,"syntology":{"n":11,"n_ran":1,"n_unverified":10,"n_pointer_only":0}},{"url":"/paper/microsoft-coco-common-objects-in-context","title":"Microsoft COCO: Common Objects in Context","date":"2014-05-01","arxiv_id":"1405.0312","repositories_listed":38,"syntology":{"n":7,"n_ran":1,"n_unverified":6,"n_pointer_only":0}},{"url":"/paper/end-to-end-object-detection-with-transformers","title":"End-to-End Object Detection with Transformers","date":"2020-05-26","arxiv_id":"2005.12872","repositories_listed":37,"syntology":{"n":92,"n_ran":59,"n_unverified":33,"n_pointer_only":19}},{"url":"/paper/striving-for-simplicity-the-all-convolutional","title":"Striving for Simplicity: The All Convolutional Net","date":"2014-12-21","arxiv_id":"1412.6806","repositories_listed":37,"syntology":{"n":6,"n_ran":0,"n_unverified":6,"n_pointer_only":0}},{"url":"/paper/spatial-memory-for-context-reasoning-in","title":"Spatial Memory for Context Reasoning in Object Detection","date":"2017-04-13","arxiv_id":"1704.04224","repositories_listed":35,"syntology":null},{"url":"/paper/a-simple-baseline-for-multi-object-tracking","title":"FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking","date":"2020-04-04","arxiv_id":"2004.01888","repositories_listed":33,"syntology":{"n":53,"n_ran":8,"n_unverified":45,"n_pointer_only":0}},{"url":"/paper/fast-r-cnn","title":"Fast R-CNN","date":"2015-04-30","arxiv_id":"1504.08083","repositories_listed":30,"syntology":{"n":8,"n_ran":5,"n_unverified":3,"n_pointer_only":1}},{"url":"/paper/deformable-convnets-v2-more-deformable-better","title":"Deformable ConvNets v2: More Deformable, Better Results","date":"2018-11-27","arxiv_id":"1811.11168","repositories_listed":26,"syntology":{"n":13,"n_ran":2,"n_unverified":11,"n_pointer_only":2}},{"url":"/paper/point-transformer-1","title":"Point Transformer","date":"2020-12-16","arxiv_id":"2012.09164","repositories_listed":24,"syntology":null},{"url":"/paper/group-normalization","title":"Group Normalization","date":"2018-03-22","arxiv_id":"1803.08494","repositories_listed":22,"syntology":{"n":15,"n_ran":5,"n_unverified":10,"n_pointer_only":4}},{"url":"/paper/yolov7-trainable-bag-of-freebies-sets-new","title":"YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","date":"2022-07-06","arxiv_id":"2207.02696","repositories_listed":21,"syntology":{"n":11,"n_ran":1,"n_unverified":10,"n_pointer_only":0}},{"url":"/paper/high-resolution-image-synthesis-and-semantic","title":"High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs","date":"2017-11-30","arxiv_id":"1711.11585","repositories_listed":21,"syntology":{"n":5,"n_ran":4,"n_unverified":1,"n_pointer_only":3}},{"url":"/paper/centernet-object-detection-with-keypoint","title":"CenterNet: Keypoint Triplets for Object Detection","date":"2019-04-17","arxiv_id":"1904.08189","repositories_listed":20,"syntology":{"n":11,"n_ran":2,"n_unverified":9,"n_pointer_only":2}},{"url":"/paper/pointpillars-fast-encoders-for-object","title":"PointPillars: Fast Encoders for Object Detection from Point Clouds","date":"2018-12-14","arxiv_id":"1812.05784","repositories_listed":18,"syntology":{"n":15,"n_ran":2,"n_unverified":13,"n_pointer_only":1}},{"url":"/paper/single-shot-refinement-neural-network-for","title":"Single-Shot Refinement Neural Network for Object Detection","date":"2017-11-18","arxiv_id":"1711.06897","repositories_listed":16,"syntology":null},{"url":"/paper/texture-synthesis-using-convolutional-neural","title":"Texture Synthesis Using Convolutional Neural Networks","date":"2015-05-27","arxiv_id":"1505.07376","repositories_listed":16,"syntology":{"n":3,"n_ran":2,"n_unverified":1,"n_pointer_only":3}},{"url":"/paper/rtmdet-an-empirical-study-of-designing-real","title":"RTMDet: An Empirical Study of Designing Real-Time Object Detectors","date":"2022-12-14","arxiv_id":"2212.07784","repositories_listed":14,"syntology":{"n":20,"n_ran":3,"n_unverified":17,"n_pointer_only":0}},{"url":"/paper/revisiting-foreground-background-imbalance-in","title":"Is Heuristic Sampling Necessary in Training Deep Object Detectors?","date":"2019-09-11","arxiv_id":"1909.04868","repositories_listed":14,"syntology":null}],"syntology_records":26,"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":0,"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"}}