{"url":"/dataset/places-lt","name":"Places-LT","full_name":null,"description_markdown":"**Places-LT** has an imbalanced training set with 62,500 images for 365 classes from Places-2. The class frequencies follow a natural power law distribution with a maximum number of 4,980 images per class and a minimum number of 5 images per class. The validation and testing sets are balanced and contain 20 and 100 images per class respectively.\r\n\r\nSource: [Long-Tailed Recognition Using Class-Balanced Experts](https://arxiv.org/abs/2004.03706)","description_withheld":null,"homepage":"https://liuziwei7.github.io/projects/LongTail.html","introduced_date":"2019-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/large-scale-long-tailed-recognition-in-an","title":"Large-Scale Long-Tailed Recognition in an Open World","first_author":"Ziwei Liu","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Long-tail Learning","url":"/task/long-tail-learning","datasets_with_task":"/datasets/task/long-tail-learning"}],"languages":[],"variants":["Places-LT"],"data_loaders":[],"num_papers_in_archive":80,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/long-tail-learning-on-places-lt","task":"Long-tail Learning","dataset_variant":"Places-LT","rows":29,"metrics":["Top-1 Accuracy","Top 1 Accuracy"],"first_row_in_archive_order":{"model":"LIFT (ViT-L/14)","paper":"/paper/parameter-efficient-long-tailed-recognition","metrics":{"Top-1 Accuracy":"53.7"},"code_links":[{"title":"shijxcs/lift","url":"https://github.com/shijxcs/lift"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/adaptive-parametric-activation","title":"Adaptive Parametric Activation","date":"2024-07-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/parameter-efficient-long-tailed-recognition","title":"Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts","date":"2023-09-18","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":5,"samples_unverified":3,"pointer_only_for_licence":8,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/improving-image-recognition-by-retrieving","title":"Improving Image Recognition by Retrieving from Web-Scale Image-Text Data","date":"2023-04-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/generalized-parametric-contrastive-learning","title":"Generalized Parametric Contrastive Learning","date":"2022-09-26","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/difficulty-net-learning-to-predict-difficulty","title":"Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition","date":"2022-09-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/nested-collaborative-learning-for-long-tailed","title":"Nested Collaborative Learning for Long-Tailed Visual Recognition","date":"2022-03-29","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":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/retrieval-augmented-classification-for-long","title":"Retrieval Augmented Classification for Long-Tail Visual Recognition","date":"2022-02-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/pure-noise-to-the-rescue-of-insufficient-data","title":"Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images","date":"2021-12-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-simple-long-tailed-recognition-baseline-via","title":"A Simple Long-Tailed Recognition Baseline via Vision-Language Model","date":"2021-11-29","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/vl-ltr-learning-class-wise-visual-linguistic","title":"VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition","date":"2021-11-26","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/parametric-contrastive-learning","title":"Parametric Contrastive Learning","date":"2021-07-26","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/test-agnostic-long-tailed-recognition-by-test","title":"Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition","date":"2021-07-20","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":4,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rsg-a-simple-but-effective-module-for","title":"RSG: A Simple but Effective Module for Learning Imbalanced Datasets","date":"2021-06-18","rows_on_this_dataset":1,"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/distribution-alignment-a-unified-framework","title":"Distribution Alignment: A Unified Framework for Long-tail Visual Recognition","date":"2021-03-30","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/disentangling-label-distribution-for-long","title":"Disentangling Label Distribution for Long-tailed Visual Recognition","date":"2020-12-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/feature-space-augmentation-for-long-tailed","title":"Feature Space Augmentation for Long-Tailed Data","date":"2020-08-09","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/balanced-meta-softmax-for-long-tailed-visual","title":"Balanced Meta-Softmax for Long-Tailed Visual Recognition","date":"2020-07-21","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/inflated-episodic-memory-with-region-self","title":"Inflated Episodic Memory With Region Self-Attention for Long-Tailed Visual Recognition","date":"2020-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/long-tailed-recognition-using-class-balanced","title":"Long-Tailed Recognition Using Class-Balanced Experts","date":"2020-04-07","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":1,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/long-tail-learning-with-attributes","title":"From Generalized zero-shot learning to long-tail with class descriptors","date":"2020-04-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rethinking-class-balanced-methods-for-long","title":"Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective","date":"2020-03-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-from-multiple-experts-self-paced","title":"Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification","date":"2020-01-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/decoupling-representation-and-classifier-for","title":"Decoupling Representation and Classifier for Long-Tailed Recognition","date":"2019-10-21","rows_on_this_dataset":1,"code_links":4,"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/large-scale-long-tailed-recognition-in-an","title":"Large-Scale Long-Tailed Recognition in an Open World","date":"2019-04-10","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":10,"samples_harvested":39,"samples_ran":19,"samples_unverified":20,"pointer_only_for_licence":16,"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."}