2015-09-25 Surveillance-nature images are released in the download links as "sv_data.*". Download all such files, then unzip them with the same password as the web-nature data. We also conducted a fine-grained classification experiment for this part of data. The results are provided in the arXiv paper.
2015-06-30 As an extension to our CVPR paper, we conduct experiments for fine-grained car classification, attribute prediction, and car verification with the entire dataset and different deep models. See arXiv paper for the details. Train/test splits can be downloaded here. The fine-tuned GoogLeNet model is uploaded to the Caffe Model Zoo.
2015-05-16 First release of CompCars, surveillance-nature images are still under organization and will be released shortly.
This dataset is presented in our CVPR 2015 paper,
Linjie Yang, Ping Luo, Chen Change Loy, Xiaoou Tang. A Large-Scale Car Dataset for Fine-Grained Categorization and Verification, In Computer Vision and Pattern Recognition (CVPR), 2015. PDF
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. Please refer to our paper for the details.
The dataset is well prepared for the following computer vision tasks:
The train/test subsets of these tasks introduced in our paper are included in the dataset. Researchers are also welcome to use it for any other tasks such as image ranking, multi-task learning, and 3D reconstruction.
Check this file for the instruction.
Stanford Cars Dataset
Labeled Faces in the Wild