Comprehensive Cars
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.
The dataset can be used for the tasks of:
The dataset can be also used for other tasks such as image ranking, multi-task learning, and 3D reconstruction.
Variants: CompCars
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Fine-Grained Image Classification | Resnet50 + PMAL | Progressive Multi-task Anti-Noise Learning and … | 2024-01-25 |
| Fine-Grained Image Classification | Fine-Tuning DARTS | Fine-Tuning DARTS for Image Classification | 2020-06-16 |
| Fine-Grained Image Classification | A3M | Attribute-Aware Attention Model for Fine-grained … | 2019-01-02 |
| Fine-Grained Image Classification | GoogLeNet | A Large-Scale Car Dataset for … | 2015-06-30 |
| Fine-Grained Image Classification | AlexNet | A Large-Scale Car Dataset for … | 2015-06-30 |
Recent papers with results on this dataset: