{"url":"/dataset/50-salads","name":"50 Salads","full_name":null,"description_markdown":"Activity recognition research has shifted focus from distinguishing full-body motion patterns to recognizing complex interactions of multiple entities. Manipulative gestures – characterized by interactions between hands, tools, and manipulable objects – frequently occur in food preparation, manufacturing, and assembly tasks, and have a variety of applications including situational support, automated supervision, and skill assessment. With the aim to stimulate research on recognizing manipulative gestures we introduce the 50 Salads dataset. It captures 25 people preparing 2 mixed salads each and contains over 4h of annotated accelerometer and RGB-D video data. Including detailed annotations, multiple sensor types, and two sequences per participant, the 50 Salads dataset may be used for research in areas such as activity recognition, activity spotting, sequence analysis, progress tracking, sensor fusion, transfer learning, and user-adaptation.\r\n\r\nThe dataset includes\r\n\r\n    RGB video data 640×480 pixels at 30 Hz\r\n    Depth maps 640×480 pixels at 30 Hz\r\n    3-axis accelerometer data at 50 Hz of devices attached to a knife, a mixing spoon, a small spoon, a peeler, a glass, an oil bottle, and a pepper dispenser.\r\n    Synchronization parameters for temporal alignment of video and accelerometer data\r\n    Annotations as temporal intervals of pre- core- and post-phases of activities corresponding to steps in a recipe","description_withheld":null,"homepage":"https://cvip.computing.dundee.ac.uk/datasets/foodpreparation/50salads/","introduced_date":"2013-09-08","introduced_date_note":null,"introduced_by":null,"license":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License","url":null},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Action Segmentation","url":"/task/action-segmentation","datasets_with_task":"/datasets/task/action-segmentation"},{"name":"Unsupervised Action Segmentation","url":"/task/unsupervised-action-segmentation","datasets_with_task":"/datasets/task/unsupervised-action-segmentation"}],"languages":[],"variants":["50 Salads"],"data_loaders":[],"num_papers_in_archive":35,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/action-segmentation-on-50-salads-1","task":"Action Segmentation","dataset_variant":"50 Salads","rows":28,"metrics":["F1@50%","F1@25%","F1@10%","Acc","Edit"],"first_row_in_archive_order":{"model":"Br-Prompt+ASPnet (RGB, flow, accelerometer)","paper":"/paper/aspnet-action-segmentation-with-shared","metrics":{"Acc":"91.4","Edit":"87.5","F1@10%":"92.7","F1@25%":"91.6","F1@50%":"88.5"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unsupervised-action-segmentation-on-50-salads","task":"Unsupervised Action Segmentation","dataset_variant":"50 Salads","rows":3,"metrics":["Acc","F1"],"first_row_in_archive_order":{"model":"LSTM+AL","paper":"/paper/a-perceptual-prediction-framework-for-self","metrics":{"Acc":"60.6"},"code_links":[{"title":"CVPRUSFTampa/EventSegmentation","url":"https://github.com/CVPRUSFTampa/EventSegmentation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/efficient-temporal-action-segmentation-via","title":"Efficient Temporal Action Segmentation via Boundary-aware Query Voting","date":"2024-05-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/is-weakly-supervised-action-segmentation","title":"Is Weakly-supervised Action Segmentation Ready For Human-Robot Interaction? No, Let's Improve It With Action-union Learning","date":"2023-10-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/how-much-temporal-long-term-context-is-needed","title":"How Much Temporal Long-Term Context is Needed for Action Segmentation?","date":"2023-08-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":6,"samples_unverified":0,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/sf-tmn-slowfast-temporal-modeling-network-for","title":"SF-TMN: SlowFast Temporal Modeling Network for Surgical Phase Recognition","date":"2023-06-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/diffusion-action-segmentation","title":"Diffusion Action Segmentation","date":"2023-03-31","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":5,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/aspnet-action-segmentation-with-shared","title":"ASPnet: Action Segmentation With Shared-Private Representation of Multiple Data Sources","date":"2023-01-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/semantic2graph-graph-based-multi-modal","title":"Semantic2Graph: Graph-based Multi-modal Feature Fusion for Action Segmentation in Videos","date":"2022-09-13","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/unified-fully-and-timestamp-supervised","title":"Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation","date":"2022-09-01","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/efficient-u-transformer-with-boundary-aware","title":"Do we really need temporal convolutions in action segmentation?","date":"2022-05-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cross-enhancement-transformer-for-action","title":"Cross-Enhancement Transformer for Action Segmentation","date":"2022-05-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/maximization-and-restoration-action","title":"Maximization and restoration: Action segmentation through dilation passing and temporal reconstruction","date":"2022-05-02","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/bridge-prompt-towards-ordinal-action","title":"Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos","date":"2022-03-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/asformer-transformer-for-action-segmentation","title":"ASFormer: Transformer for Action Segmentation","date":"2021-10-16","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unsupervised-activity-segmentation-by-joint","title":"Unsupervised Action Segmentation by Joint Representation Learning and Online Clustering","date":"2021-05-27","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/coarse-to-fine-multi-resolution-temporal","title":"Coarse to Fine Multi-Resolution Temporal Convolutional Network","date":"2021-05-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/efficient-two-step-networks-for-temporal","title":"Efficient Two-Step Networks for Temporal Action Segmentation","date":"2021-04-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/action-segmentation-with-mixed-temporal","title":"Action Segmentation with Mixed Temporal Domain Adaptation","date":"2021-04-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/temporally-weighted-hierarchical-clustering","title":"Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation","date":"2021-03-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/depthwise-separable-temporal-convolutional","title":"Depthwise Separable Temporal Convolutional Network for Action Segmentation","date":"2021-01-19","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/global2local-efficient-structure-search-for","title":"Global2Local: Efficient Structure Search for Video Action Segmentation","date":"2021-01-04","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/refining-action-segmentation-with","title":"Refining Action Segmentation With Hierarchical Video Representations","date":"2021-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/temporal-relational-modeling-with-self","title":"Temporal Relational Modeling with Self-Supervision for Action Segmentation","date":"2020-12-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/boundary-aware-cascade-networks-for-temporal","title":"Boundary-Aware Cascade Networks for Temporal Action Segmentation","date":"2020-08-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/alleviating-over-segmentation-errors-by","title":"Alleviating Over-segmentation Errors by Detecting Action Boundaries","date":"2020-07-14","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":0,"samples_unverified":11,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ms-tcn-multi-stage-temporal-convolutional-2","title":"MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation","date":"2020-06-16","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/action-segmentation-with-joint-self","title":"Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation","date":"2020-03-05","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ms-tcn-multi-stage-temporal-convolutional","title":"MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation","date":"2019-03-05","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/a-perceptual-prediction-framework-for-self","title":"A Perceptual Prediction Framework for Self Supervised Event Segmentation","date":"2018-11-12","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"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":11,"samples_harvested":47,"samples_ran":27,"samples_unverified":20,"pointer_only_for_licence":14,"papers_with_no_sample_that_ran":3,"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."}