{"url":"/sota/fine-grained-image-classification-on-10","task":{"name":"Fine-Grained Image Classification","url":"/task/fine-grained-image-classification","note":null},"dataset":{"name":"10 Monkey Species","url":null},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Fine-Grained Image Classification** is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Looking for the Devil in the Details](https://arxiv.org/pdf/1903.06150v2.pdf) )</span>","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":["Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher"}},"counts":{"rows":4,"rows_with_code":3,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"Inception-v3 (Spinal FC)","metrics":{"Accuracy":"99.26"},"uses_additional_data":false,"paper_date":"2021-10-14","paper":"/paper/a-comprehensive-study-on-torchvision-pre","paper_url":"https://arxiv.org/abs/2110.07097v1","paper_title":"A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification","code":"https://github.com/dipuk0506/SpinalNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"WideResNet-101(Spinal FC)","metrics":{"Accuracy":"99.26"},"uses_additional_data":false,"paper_date":"2021-10-14","paper":"/paper/a-comprehensive-study-on-torchvision-pre","paper_url":"https://arxiv.org/abs/2110.07097v1","paper_title":"A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification","code":"https://github.com/dipuk0506/SpinalNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"VGG-19_bn","metrics":{"Accuracy":"98.90"},"uses_additional_data":false,"paper_date":"2021-10-14","paper":"/paper/a-comprehensive-study-on-torchvision-pre","paper_url":"https://arxiv.org/abs/2110.07097v1","paper_title":"A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification","code":"https://github.com/dipuk0506/SpinalNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"CNN","metrics":{"Accuracy":"95.00"},"uses_additional_data":false,"paper_date":"2020-06-28","paper":"/paper/performing-image-classification-for-10","paper_url":"https://d1wqtxts1xzle7.cloudfront.net/59558097/Project_final_documentation20190606-52603-1sp6ali.pdf?1559856244=&response-content-disposition=inline%3B+filename%3DProject_final_documentation.pdf&Expires=1601994830&Signature=Zn6Skx93dTC60aMhHG3JL~6NYh38zoXmGZ5hWnnbk7YyX5OtJ~7UMohmYqmeUMcD2uXTypHc9s3wmH9-sKPMuLXQIzCRaezw5R~C7j613Ky8~lZ8vgZVhTdnbVlKqxKVoXleCqOr~eoxcnUmx-uaU1ALfqyr69154z-JM3kM7UwAAw2MeFtmYSR3Xk5eKFwQsdWkJkW5ZrD6FTfNKOdMhNhF93dRc41ufeff0oFR6O7jY7EejNVBk6VxwdxF3ZCAH33t8DYKLL63ICE9vm~QgeuKM~eGGpj5Tkq3FZXVajiGHGkJrEvwe2TISjPWaD3AGExlASEFdO5MP4gGvcyNrQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA","paper_title":"Performing Image Classification for 10 Different Monkey Species using CNN","code":null,"n_code_links":0,"syntology":null}],"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":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"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"}}}