{"url":"/dataset/compcars","name":"CompCars","full_name":"Comprehensive Cars","description_markdown":"The **Comprehensive Cars (CompCars)** dataset contains data from two scenarios, including images from web-nature and surveillance-nature. The web-nature data contains 163 car makes with 1,716 car models. There are a total of 136,726 images capturing the entire cars and 27,618 images capturing the car parts. The full car images are labeled with bounding boxes and viewpoints. Each car model is labeled with five attributes, including maximum speed, displacement, number of doors, number of seats, and type of car. The surveillance-nature data contains 50,000 car images captured in the front view. \r\n\r\nThe dataset can be used for the tasks of:\r\n\r\n- Fine-grained classification\r\n- Attribute prediction\r\n- Car model verification\r\n\r\nThe dataset can be also used for other tasks such as image ranking, multi-task learning, and 3D reconstruction.","description_withheld":null,"homepage":"http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/a-large-scale-car-dataset-for-fine-grained","title":"A Large-Scale Car Dataset for Fine-Grained Categorization and Verification","first_author":"Linjie Yang","url":null},"license":{"name":"Custom (research-only, non-commercial)","url":"http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Fine-Grained Image Classification","url":"/task/fine-grained-image-classification","datasets_with_task":"/datasets/task/fine-grained-image-classification"}],"languages":[],"variants":["CompCars"],"data_loaders":[{"repo":"https://github.com/Graviti-AI/datasets","url":"https://gas.graviti.com/dataset/graviti/CompCars","frameworks":["tf","pytorch"]}],"num_papers_in_archive":70,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/fine-grained-image-classification-on-compcars","task":"Fine-Grained Image Classification","dataset_variant":"CompCars","rows":7,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Resnet50 + PMAL","paper":"/paper/progressive-multi-task-anti-noise-learning","metrics":{"Accuracy":"99.1%"},"code_links":[{"title":"dichao-liu/anti-noise_fgvr","url":"https://github.com/dichao-liu/anti-noise_fgvr"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/progressive-multi-task-anti-noise-learning","title":"Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition","date":"2024-01-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fine-tuning-darts-for-image-classification","title":"Fine-Tuning DARTS for Image Classification","date":"2020-06-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/fine-grained-vehicle-classification-with","title":"Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning","date":"2019-01-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/attribute-aware-attention-model-for-fine","title":"Attribute-Aware Attention Model for Fine-grained Representation Learning","date":"2019-01-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-cnns-with-spatially-weighted-pooling-for","title":"Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition","date":"2017-04-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-large-scale-car-dataset-for-fine-grained","title":"A Large-Scale Car Dataset for Fine-Grained Categorization and Verification","date":"2015-06-30","rows_on_this_dataset":2,"code_links":3,"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."}