{"url":"/dataset/fewsol","name":"FewSOL","full_name":"A Dataset for Few-Shot Object Learning in Robotic Environments","description_markdown":"The **Few**-**S**hot **O**bject **L**earning (FewSOL) dataset can be used for object recognition with a few images per object. It contains 336 real-world objects with 9 RGB-D images per object from different views. Object segmentation masks, object poses and object attributes are provided. In addition, synthetic images generated using 330 3D object models are used to augment the dataset.  FewSOL dataset can be used to study a set of few-shot object recognition problems such as classification, detection and segmentation, shape reconstruction, pose estimation, keypoint correspondences and attribute recognition. \r\n\r\n**Motivation**: If robots can recognize objects from a few exemplar images, it is possible to scale up the number of objects a robot can recognize because collecting a few images per object is a much easier process compared to building a 3D model of an object. In addition, models trained in the meta-learning setting can generalize to new objects without re-training.","description_withheld":null,"homepage":"https://irvlutd.github.io/FewSOL","introduced_date":"2022-07-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/fewsol-a-dataset-for-few-shot-object-learning","title":"FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments","first_author":"Jishnu Jaykumar P","url":null},"license":{"name":"MIT","url":"https://utdallas.box.com/v/FewSOL-LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"},{"name":"6D","url":"/datasets/modality/6d"}],"tasks":[{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"},{"name":"Few-Shot Image Classification","url":"/task/few-shot-image-classification","datasets_with_task":"/datasets/task/few-shot-image-classification"},{"name":"Few-Shot Learning","url":"/task/few-shot-learning","datasets_with_task":"/datasets/task/few-shot-learning"},{"name":"Few-Shot Semantic Segmentation","url":"/task/few-shot-image-segmentation","datasets_with_task":"/datasets/task/few-shot-image-segmentation"},{"name":"Object Recognition","url":"/task/object-recognition","datasets_with_task":"/datasets/task/object-recognition"},{"name":"Image-to-Text Retrieval","url":"/task/image-to-text-retrieval","datasets_with_task":"/datasets/task/image-to-text-retrieval"},{"name":"Zero-shot Text-to-Image Retrieval","url":"/task/zero-shot-text-to-image-retrieval","datasets_with_task":"/datasets/task/zero-shot-text-to-image-retrieval"},{"name":"3D Shape Reconstruction","url":"/task/3d-shape-reconstruction","datasets_with_task":"/datasets/task/3d-shape-reconstruction"},{"name":"Image-text Classification","url":"/task/image-text-classification","datasets_with_task":"/datasets/task/image-text-classification"},{"name":"Key Point Matching","url":"/task/key-point-matching","datasets_with_task":"/datasets/task/key-point-matching"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["FewSOL"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"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."}