{"url":"/sota/classification-on-indl","task":{"name":"Classification","url":"/task/classification-1","note":null},"dataset":{"name":"InDL","url":"/dataset/indl"},"category":null,"categories":["Adversarial","Audio","Computer Code","Computer Vision","Graphs","Medical","Methodology","Miscellaneous","Natural Language Processing","Reasoning","Time Series"],"category_note":"the archive's category list for this table covers most areas; treated as no area assigned","description":"**Classification** is the task of categorizing a set of data into predefined classes or groups. The aim of classification is to train a model to correctly predict the class or group of new, unseen data. The model is trained on a labeled dataset where each instance is assigned a class label. The learning algorithm then builds a mapping between the features of the data and the class labels. This mapping is then used to predict the class label of new, unseen data points. The quality of the prediction is usually evaluated using metrics such as accuracy, precision, and recall.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Average Recall"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Average Recall":"higher"}},"counts":{"rows":9,"rows_with_code":9,"rows_with_paper_page":9,"rows_dated":9,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"ConvNext","metrics":{"Average Recall":"93.47%"},"uses_additional_data":false,"paper_date":"2022-01-10","paper":"/paper/a-convnet-for-the-2020s","paper_url":"https://arxiv.org/abs/2201.03545v2","paper_title":"A ConvNet for the 2020s","code":"https://github.com/keras-team/keras/blob/master/keras/applications/convnext.py","n_code_links":54,"syntology":{"n_ran":54,"n_unverified":26,"n_samples":80,"n_pointer_only_licence":11}},{"rank_in_archive_order":2,"model":"VGG16","metrics":{"Average Recall":"92.86%"},"uses_additional_data":false,"paper_date":"2014-09-04","paper":"/paper/very-deep-convolutional-networks-for-large","paper_url":"http://arxiv.org/abs/1409.1556v6","paper_title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","code":"https://github.com/tensorflow/models/tree/master/research/slim","n_code_links":305,"syntology":{"n_ran":12,"n_unverified":110,"n_samples":122,"n_pointer_only_licence":4}},{"rank_in_archive_order":3,"model":"DenseNet201","metrics":{"Average Recall":"90.99%"},"uses_additional_data":false,"paper_date":"2016-08-25","paper":"/paper/densely-connected-convolutional-networks","paper_url":"http://arxiv.org/abs/1608.06993v5","paper_title":"Densely Connected Convolutional Networks","code":"https://github.com/pytorch/vision","n_code_links":146,"syntology":{"n_ran":18,"n_unverified":53,"n_samples":71,"n_pointer_only_licence":7}},{"rank_in_archive_order":4,"model":"Inception ResNet V2","metrics":{"Average Recall":"90.27%"},"uses_additional_data":false,"paper_date":"2016-02-23","paper":"/paper/inception-v4-inception-resnet-and-the-impact","paper_url":"http://arxiv.org/abs/1602.07261v2","paper_title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","code":"https://github.com/tensorflow/models","n_code_links":87,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"Xception","metrics":{"Average Recall":"89.81%"},"uses_additional_data":false,"paper_date":"2016-10-07","paper":"/paper/xception-deep-learning-with-depthwise","paper_url":"http://arxiv.org/abs/1610.02357v3","paper_title":"Xception: Deep Learning with Depthwise Separable Convolutions","code":"https://github.com/tensorflow/models/tree/master/research/deeplab","n_code_links":41,"syntology":{"n_ran":1,"n_unverified":14,"n_samples":15,"n_pointer_only_licence":0}},{"rank_in_archive_order":6,"model":"NASNetLarge","metrics":{"Average Recall":"89.52%"},"uses_additional_data":false,"paper_date":"2017-07-21","paper":"/paper/learning-transferable-architectures-for","paper_url":"http://arxiv.org/abs/1707.07012v4","paper_title":"Learning Transferable Architectures for Scalable Image Recognition","code":"https://github.com/tensorflow/models/tree/master/research/slim","n_code_links":17,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":7,"model":"Darknet53","metrics":{"Average Recall":"88.53%"},"uses_additional_data":false,"paper_date":"2018-04-08","paper":"/paper/yolov3-an-incremental-improvement","paper_url":"http://arxiv.org/abs/1804.02767v1","paper_title":"YOLOv3: An Incremental Improvement","code":"https://github.com/open-mmlab/mmdetection","n_code_links":311,"syntology":{"n_ran":18,"n_unverified":106,"n_samples":124,"n_pointer_only_licence":19}},{"rank_in_archive_order":8,"model":"ResNetV2_50","metrics":{"Average Recall":"88.08%"},"uses_additional_data":false,"paper_date":"2021-10-01","paper":"/paper/resnet-strikes-back-an-improved-training","paper_url":"https://arxiv.org/abs/2110.00476v1","paper_title":"ResNet strikes back: An improved training procedure in timm","code":"https://github.com/rwightman/pytorch-image-models","n_code_links":14,"syntology":{"n_ran":0,"n_unverified":3,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":9,"model":"MobileNetV3","metrics":{"Average Recall":"84.28%"},"uses_additional_data":false,"paper_date":"2019-05-06","paper":"/paper/searching-for-mobilenetv3","paper_url":"https://arxiv.org/abs/1905.02244v5","paper_title":"Searching for MobileNetV3","code":"https://github.com/tensorflow/models","n_code_links":67,"syntology":{"n_ran":58,"n_unverified":47,"n_samples":105,"n_pointer_only_licence":46}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":9,"rows_with_any_sample_ran":7,"distinct_papers_with_graph_line":9,"distinct_papers_with_any_sample_ran":7,"samples_over_distinct_papers":{"n_ran":162,"n_unverified":360,"n_samples":522,"n_pointer_only_licence":88,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":162,"n_unverified":360,"n_samples":522,"n_pointer_only_licence":88,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}