{"url":"/dataset/bsds500","name":"BSDS500","full_name":"Berkeley Segmentation Dataset 500","description_markdown":"Berkeley Segmentation Data Set 500 (**BSDS500**) is a standard benchmark for contour detection. This dataset is designed for evaluating natural edge detection that includes not only object contours but also object interior boundaries and background boundaries. It includes 500 natural images with carefully annotated boundaries collected from multiple users. The dataset is divided into three parts: 200 for training, 100 for validation and the rest 200 for test.\r\n\r\nSource: [Object Contour Detection with a Fully Convolutional Encoder-Decoder Network](https://arxiv.org/abs/1603.04530)\r\nImage Source: [https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html)","description_withheld":null,"homepage":"https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html","introduced_date":"2011-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Contour Detection and Hierarchical Image Segmentation","first_author":null,"url":"https://doi.org/10.1109/TPAMI.2010.161"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"}],"tasks":[{"name":"JPEG Artifact Correction","url":"/task/jpeg-artifact-correction","datasets_with_task":"/datasets/task/jpeg-artifact-correction"},{"name":"Image Compression","url":"/task/image-compression","datasets_with_task":"/datasets/task/image-compression"},{"name":"Edge Detection","url":"/task/edge-detection","datasets_with_task":"/datasets/task/edge-detection"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["BSDS500 (Quality 30 Grayscale)","BSDS500 (Quality 30 Color)","BSDS500 (Quality 20 Grayscale)","BSDS500 (Quality 20 Color)","BSDS500 (Quality 10 Grayscale)","BSDS500 (Quality 10 Color)","BSDS500"],"data_loaders":[{"repo":"https://github.com/mengyangpu/edter","url":"https://github.com/mengyangpu/edter","frameworks":["pytorch"]}],"num_papers_in_archive":261,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/jpeg-artifact-correction-on-bsds500-quality","task":"JPEG Artifact Correction","dataset_variant":"BSDS500 (Quality 10 Color)","rows":2,"metrics":["PSNR","PSNR-B","SSIM"],"first_row_in_archive_order":{"model":"FBCNN","paper":"/paper/towards-flexible-blind-jpeg-artifacts-removal","metrics":{"PSNR":"27.85","PSNR-B":"27.52","SSIM":"0.799"},"code_links":[{"title":"jiaxi-jiang/fbcnn","url":"https://github.com/jiaxi-jiang/fbcnn"},{"title":"olaviinha/NeuralImageSuperResolution","url":"https://github.com/olaviinha/NeuralImageSuperResolution"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/jpeg-artifact-correction-on-bsds500-quality-1","task":"JPEG Artifact Correction","dataset_variant":"BSDS500 (Quality 20 Color)","rows":2,"metrics":["PSNR","PSNR-B","SSIM"],"first_row_in_archive_order":{"model":"FBCNN","paper":"/paper/towards-flexible-blind-jpeg-artifacts-removal","metrics":{"PSNR":"30.14","PSNR-B":"29.56","SSIM":"0.867"},"code_links":[{"title":"jiaxi-jiang/fbcnn","url":"https://github.com/jiaxi-jiang/fbcnn"},{"title":"olaviinha/NeuralImageSuperResolution","url":"https://github.com/olaviinha/NeuralImageSuperResolution"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/jpeg-artifact-correction-on-bsds500-quality-2","task":"JPEG Artifact Correction","dataset_variant":"BSDS500 (Quality 30 Color)","rows":2,"metrics":["PSNR","PSNR-B","SSIM"],"first_row_in_archive_order":{"model":"FBCNN","paper":"/paper/towards-flexible-blind-jpeg-artifacts-removal","metrics":{"PSNR":"31.45","PSNR-B":"30.72","SSIM":"0.897"},"code_links":[{"title":"jiaxi-jiang/fbcnn","url":"https://github.com/jiaxi-jiang/fbcnn"},{"title":"olaviinha/NeuralImageSuperResolution","url":"https://github.com/olaviinha/NeuralImageSuperResolution"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/jpeg-artifact-correction-on-bsds500-quality-3","task":"JPEG Artifact Correction","dataset_variant":"BSDS500 (Quality 10 Grayscale)","rows":2,"metrics":["PSNR","PSNR-B","SSIM"],"first_row_in_archive_order":{"model":"FBCNN","paper":"/paper/towards-flexible-blind-jpeg-artifacts-removal","metrics":{"PSNR":"29.67","PSNR-B":"29.22","SSIM":"0.821"},"code_links":[{"title":"jiaxi-jiang/fbcnn","url":"https://github.com/jiaxi-jiang/fbcnn"},{"title":"olaviinha/NeuralImageSuperResolution","url":"https://github.com/olaviinha/NeuralImageSuperResolution"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/jpeg-artifact-correction-on-bsds500-quality-4","task":"JPEG Artifact Correction","dataset_variant":"BSDS500 (Quality 20 Grayscale)","rows":2,"metrics":["PSNR","PSNR-B","SSIM"],"first_row_in_archive_order":{"model":"FBCNN","paper":"/paper/towards-flexible-blind-jpeg-artifacts-removal","metrics":{"PSNR":"32.00","PSNR-B":"31.19","SSIM":"0.885"},"code_links":[{"title":"jiaxi-jiang/fbcnn","url":"https://github.com/jiaxi-jiang/fbcnn"},{"title":"olaviinha/NeuralImageSuperResolution","url":"https://github.com/olaviinha/NeuralImageSuperResolution"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/jpeg-artifact-correction-on-bsds500-quality-5","task":"JPEG Artifact Correction","dataset_variant":"BSDS500 (Quality 30 Grayscale)","rows":2,"metrics":["PSNR","PSNR-B","SSIM"],"first_row_in_archive_order":{"model":"FBCNN","paper":"/paper/towards-flexible-blind-jpeg-artifacts-removal","metrics":{"PSNR":"33.37","PSNR-B":"32.32","SSIM":"0.913"},"code_links":[{"title":"jiaxi-jiang/fbcnn","url":"https://github.com/jiaxi-jiang/fbcnn"},{"title":"olaviinha/NeuralImageSuperResolution","url":"https://github.com/olaviinha/NeuralImageSuperResolution"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/edge-detection-on-bsds500-1","task":"Edge Detection","dataset_variant":"BSDS500","rows":1,"metrics":["F1"],"first_row_in_archive_order":{"model":"RCN","paper":"/paper/object-contour-and-edge-detection-with","metrics":{"F1":"0.824"},"code_links":[{"title":"AndreKelm/RefineContourNet","url":"https://github.com/AndreKelm/RefineContourNet"},{"title":"daovietanhuet/ContourDetection","url":"https://github.com/daovietanhuet/ContourDetection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-compression-on-bsds500","task":"Image Compression","dataset_variant":"BSDS500","rows":1,"metrics":["Average PSNR","SSIM"],"first_row_in_archive_order":{"model":"RK-CCSNet","paper":"/paper/sequential-convolution-and-runge-kutta","metrics":{"Average PSNR":"30.51","SSIM":"97.66"},"code_links":[{"title":"rkteddy/RK-CCSNet","url":"https://github.com/rkteddy/RK-CCSNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/towards-flexible-blind-jpeg-artifacts-removal","title":"Towards Flexible Blind JPEG Artifacts Removal","date":"2021-09-29","rows_on_this_dataset":6,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":9,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/sequential-convolution-and-runge-kutta","title":"Sequential Convolution and Runge-Kutta Residual Architecture for Image Compressed Sensing","date":"2020-08-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/quantization-guided-jpeg-artifact-correction","title":"Quantization Guided JPEG Artifact Correction","date":"2020-04-17","rows_on_this_dataset":6,"code_links":1,"syntology":null},{"paper":"/paper/object-contour-and-edge-detection-with","title":"Object Contour and Edge Detection with RefineContourNet","date":"2019-04-30","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":11,"samples_ran":9,"samples_unverified":2,"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."}