{"url":"/dataset/caltech-256","name":"Caltech-256","full_name":null,"description_markdown":"**Caltech-256** is an object recognition dataset containing 30,607 real-world images, of different sizes, spanning 257 classes (256 object classes and an additional clutter class). Each class is represented by at least 80 images. The dataset is a superset of the Caltech-101 dataset.\r\n\r\nSource: [Exploiting Non-Linear Redundancy for Neural Model Compression](https://arxiv.org/abs/2005.14070)\r\n\r\nImage Source: [ML4A](https://twitter.com/ml4a_/status/934796379171512322)","description_withheld":null,"homepage":"http://www.vision.caltech.edu/Image_Datasets/Caltech256/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":null,"title":"Caltech-256 object category dataset","first_author":null,"url":"http://authors.library.caltech.edu/7694"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Few-Shot Image Classification","url":"/task/few-shot-image-classification","datasets_with_task":"/datasets/task/few-shot-image-classification"},{"name":"Semi-Supervised Image Classification","url":"/task/semi-supervised-image-classification","datasets_with_task":"/datasets/task/semi-supervised-image-classification"}],"languages":[],"variants":["Caltech-256, 1024 Labels","Caltech-256","Caltech-256 5-way (1-shot)"],"data_loaders":[{"repo":"https://github.com/pytorch/vision","url":"https://pytorch.org/vision/stable/generated/torchvision.datasets.Caltech256.html","frameworks":["pytorch"]},{"repo":"https://github.com/voxel51/fiftyone","url":"https://docs.voxel51.com/user_guide/dataset_zoo/datasets.html#caltech-256","frameworks":["tf","pytorch"]},{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/caltech-256-dataset","frameworks":["tf","pytorch"]}],"num_papers_in_archive":401,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-classification-on-caltech-256","task":"Image Classification","dataset_variant":"Caltech-256","rows":5,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"AG-Net","paper":"/paper/attend-and-guide-ag-net-a-keypoints-driven","metrics":{"Accuracy":"96.89%"},"code_links":[{"title":"DanielKovach/AG-Net","url":"https://github.com/DanielKovach/AG-Net"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/few-shot-image-classification-on-caltech-256","task":"Few-Shot Image Classification","dataset_variant":"Caltech-256 5-way (1-shot)","rows":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UL-Hopfield (ULH)","paper":"/paper/unsupervised-learning-using-pretrained-cnn","metrics":{"Accuracy":"74.7"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/semi-supervised-image-classification-on-10","task":"Semi-Supervised Image Classification","dataset_variant":"Caltech-256, 1024 Labels","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UL-Hopfield (ULH)","paper":"/paper/unsupervised-learning-using-pretrained-cnn","metrics":{"Accuracy":"77.40%"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/semi-supervised-image-classification-on-11","task":"Semi-Supervised Image Classification","dataset_variant":"Caltech-256","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UL-Hopfield (ULH)","paper":"/paper/unsupervised-learning-using-pretrained-cnn","metrics":{"Accuracy":"77.40%"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/mergednet-a-simple-approach-for-one-shot","title":"MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layers","date":"2022-10-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/wavemix-lite-a-resource-efficient-neural","title":"WaveMix: A Resource-efficient Neural Network for Image Analysis","date":"2022-05-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/attend-and-guide-ag-net-a-keypoints-driven","title":"Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition","date":"2021-10-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/non-binary-deep-transfer-learning-for","title":"Non-binary deep transfer learning for image classification","date":"2021-07-19","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/delta-encoder-an-effective-sample-synthesis","title":"Delta-encoder: an effective sample synthesis method for few-shot object recognition","date":"2018-06-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unsupervised-learning-using-pretrained-cnn","title":"Unsupervised Learning using Pretrained CNN and Associative Memory Bank","date":"2018-05-02","rows_on_this_dataset":3,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}