{"url":"/dataset/birdsnap","name":"Birdsnap","full_name":null,"description_markdown":"**Birdsnap** is a large bird dataset consisting of 49,829 images from 500 bird species with 47,386 images used for training and 2,443 images used for testing.\r\n\r\nSource: [Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks](https://arxiv.org/abs/1511.09209)\r\nImage Source: [http://thomasberg.org/](http://thomasberg.org/)","description_withheld":null,"homepage":"http://thomasberg.org/","introduced_date":"2014-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/birdsnap-large-scale-fine-grained-visual","title":"Birdsnap: Large-scale Fine-grained Visual Categorization of Birds","first_author":"Thomas Berg","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Clustering","url":"/task/image-clustering","datasets_with_task":"/datasets/task/image-clustering"},{"name":"Fine-Grained Image Classification","url":"/task/fine-grained-image-classification","datasets_with_task":"/datasets/task/fine-grained-image-classification"}],"languages":[],"variants":["Birdsnap"],"data_loaders":[{"repo":"https://github.com/moskomule/sam.pytorch","url":"https://github.com/moskomule/sam.pytorch","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":72,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/fine-grained-image-classification-on-birdsnap","task":"Fine-Grained Image Classification","dataset_variant":"Birdsnap","rows":5,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"EffNet-L2 (SAM)","paper":"/paper/sharpness-aware-minimization-for-efficiently-1","metrics":{"Accuracy":"90.07%"},"code_links":[{"title":"davda54/sam","url":"https://github.com/davda54/sam"},{"title":"google-research/sam","url":"https://github.com/google-research/sam"},{"title":"moskomule/sam.pytorch","url":"https://github.com/moskomule/sam.pytorch"},{"title":"simon20010923/DDAMFN","url":"https://github.com/simon20010923/DDAMFN"},{"title":"ys-zong/medfair","url":"https://github.com/ys-zong/medfair"},{"title":"sayakpaul/Sharpness-Aware-Minimization-TensorFlow","url":"https://github.com/sayakpaul/Sharpness-Aware-Minimization-TensorFlow"},{"title":"wangermeng2021/Scaled-YOLOv4-tensorflow2","url":"https://github.com/wangermeng2021/Scaled-YOLOv4-tensorflow2"},{"title":"Jannoshh/simple-sam","url":"https://github.com/Jannoshh/simple-sam"},{"title":"rollovd/LookSAM","url":"https://github.com/rollovd/LookSAM"},{"title":"wangermeng2021/FastClassification","url":"https://github.com/wangermeng2021/FastClassification"},{"title":"borealisai/perturbed-forgetting","url":"https://github.com/borealisai/perturbed-forgetting"},{"title":"mhassann22/GCSAM","url":"https://github.com/mhassann22/GCSAM"},{"title":"Janus-Shiau/SAM-tf2","url":"https://github.com/Janus-Shiau/SAM-tf2"},{"title":"NiMlr/pynlqn","url":"https://github.com/NiMlr/pynlqn"},{"title":"Ashay-20/TF-SAM-Sharpness-Aware-Minimization-Implementation","url":"https://github.com/Ashay-20/TF-SAM-Sharpness-Aware-Minimization-Implementation"},{"title":"Yuheon/Sharp-Aware-Minimization","url":"https://github.com/Yuheon/Sharp-Aware-Minimization"},{"title":"denizyuret/playground","url":"https://github.com/denizyuret/playground"},{"title":"MindCode-4/code-13","url":"https://github.com/MindCode-4/code-13/tree/main/Scalable-Sharpness-Aware-Minimization"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-clustering-on-birdsnap","task":"Image Clustering","dataset_variant":"Birdsnap","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"TURTLE (CLIP + DINOv2)","paper":"/paper/let-go-of-your-labels-with-unsupervised-1","metrics":{"Accuracy":"68.1"},"code_links":[{"title":"mlbio-epfl/turtle","url":"https://github.com/mlbio-epfl/turtle"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/let-go-of-your-labels-with-unsupervised-1","title":"Let Go of Your Labels with Unsupervised Transfer","date":"2024-06-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/sharpness-aware-minimization-for-efficiently-1","title":"Sharpness-Aware Minimization for Efficiently Improving Generalization","date":"2020-10-03","rows_on_this_dataset":1,"code_links":18,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":8,"samples_unverified":12,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fixing-the-train-test-resolution-discrepancy","title":"Fixing the train-test resolution discrepancy","date":"2019-06-14","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/gpipe-efficient-training-of-giant-neural","title":"GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism","date":"2018-11-16","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":25,"samples_ran":1,"samples_unverified":24,"pointer_only_for_licence":16,"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":6,"samples_harvested":358,"samples_ran":187,"samples_unverified":171,"pointer_only_for_licence":140,"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."}