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The Comprehensive Cars
2D Classification
许可协议: Unknown

Overview

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:

  • Fine-grained classification
  • Attribute prediction
  • Car model verification

The train/test subsets of these tasks introduced in our paper are included in the dataset. Researchers are also welcome to utilize it for any other tasks such as image ranking, multi-task learning, and 3D reconstruction.

数据概要
数据格式
image,
数据量
136.726K
文件大小
--
| 数据量 136.726K | 大小 --
The Comprehensive Cars
2D Classification
许可协议: Unknown

Overview

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:

  • Fine-grained classification
  • Attribute prediction
  • Car model verification

The train/test subsets of these tasks introduced in our paper are included in the dataset. Researchers are also welcome to utilize it for any other tasks such as image ranking, multi-task learning, and 3D reconstruction.

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