Computer Vision Datasets

• Computer Vision Datasets
  • Autonomous Driving/ADAS
    • Argoverse 2
    • The Cityscapes Dataset
    • All-In-One Drive, A Large-Scale Comprehensive Perception Dataset with High-Density Long-Range Point Clouds
    • DAIR-V2X, The world's first vehicle-road collaboration dataset release
    • Dense Depth for Autonomous Driving - DDAD dataset
    • Audi Autonomous Driving Dataset (A2D2)
    • Thermal Imaging
      • FREE Teledyne FLIR Thermal Dataset for Algorithm Training
    • Motion Planning
      • SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
    • Synthetic Dataset
      • The SYNTHIA dataset
  • Face
  • Human
  • Video Object Segmentation
  • Dataset Search Engine

Autonomous Driving/ADAS

Argoverse 2

Argoverse 2 Sensor Dataset	Argoverse 2 Motion Forecasting Dataset	Argoverse 2 Lidar Dataset	Argoverse 2 Map Change Dataset
1,000 3D annotated scenarios with lidar, stereo imagery, and ring camera imagery. This dataset improves upon the Argoverse 1 3D Tracking dataset.	250,000 scenarios with trajectory data for many object types. This dataset improves upon the Argoverse 1 Motion Forecasting Dataset.	20,000 unannotated lidar sequences.	1,000 scenarios, 200 of which depict real-world HD map changes

The Cityscapes Dataset

• 5000 images with high quality annotations
• 20000 images with coarse annotations
• 50 different cities

All-In-One Drive, A Large-Scale Comprehensive Perception Dataset with High-Density Long-Range Point Clouds

• Full sensor suite (3x LiDAR, 1x SPAD-LiDAR, 4x Radar, 5x RGB, 5x depth camera, IMU, GPS)
• 100 sequences with 1000 frames (100s) each
• 500,000 annotated images for 5 camera viewpoints
• 100,000 annotated frames for each LiDAR/Radar sensor
• 26M 2D/3D bounding boxes precisely annotated for 4 object classes (car, cyclist, motorcycle, pedestrian)
• 

DAIR-V2X, The world's first vehicle-road collaboration dataset release

• Totally 71254 LiDAR frames and 71254 Camera images:

  DAIR-V2X Cooperative Dataset (DAIR-V2X-C)	DAIR-V2X Infrastructure Dataset (DAIR-V2X-I)	DAIR-V2X Vehicle Dataset (DAIR-V2X-V)
  38845 LiDAR frames 38845 Camera images	10084 LiDAR frames 10084 Camera images	22325 LiDAR frames 22325 Camera images

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Dense Depth for Autonomous Driving - DDAD dataset

• The training and validation scenes are 5 or 10 seconds long and consist of 50 or 100 samples with corresponding Luminar-H2 pointcloud and six image frames including intrinsic and extrinsic calibration.
• The training set contains 150 scenes with a total of 12650 individual samples (75900 RGB images), and the validation set contains 50 scenes with a total of 3950 samples (23700 RGB images).
• train+val 257 GB
• 

Audi Autonomous Driving Dataset (A2D2)

• The dataset features 2D semantic segmentation, 3D point clouds, 3D bounding boxes, and vehicle bus data
• Sensor setup:

Five LiDAR sensors	Front centre camera	Surround cameras (5x)
Up to 100 m range +/- 3 cm accuracy 16 channels 10 Hz rotation rate 360° horizontal field of view +/- 15° vertical field of view	1920 × 1208 resolution 60° horizontal field of view 38° vertical field of view 30 fps framerate	1920 × 1208 resolution 120° horizontal view angle 73° vertical view angle 30 fps framerate

• 

One MILLION SCENES

The ONCE dataset is a large-scale autonomous driving dataset with 2D&3D object annotations.

• 1 Million LiDAR frames, 7 Million camera images
• 200 km² driving regions, 144 driving hours
• 15k fully annotated scenes with 5 classes (Car, Bus, Truck, Pedestrian, Cyclist)
• Diverse environments (day/night, sunny/rainy, urban/suburban areas)

Thermal Imaging

FREE Teledyne FLIR Thermal Dataset for Algorithm Training

• A total of 26,442 fully annotated frames with 520,000 bounding box annotations across 15 different object categories
• 9,711 thermal and 9,233 RGB training/validation images with a suggested training/validation split. Includes 16-bit pre-AGC frames
• 7,498 total video frames recorded at 24Hz. 1:1 match between thermal and visible frames. Includes 16-bit pre-AGC frames
• 

Motion Planning

SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies

Synthetic Dataset

The SYNTHIA dataset

• SYNTHIA consists of a collection of photo-realistic frames rendered from a virtual city and comes with precise pixel-level semantic annotations for 13 classes: misc, sky, building, road, sidewalk, fence, vegetation, pole, car, sign, pedestrian, cyclist, lane-marking.
• 

Face

Human

• Crowdhuman

Video Object Segmentation

1. Occluded Video Instance Segmentation (OVIS)

• Highlights: occluded video instances
• OVIS consists of 296k high-quality instance masks from 25 semantic categories, where object occlusions usually occur.

2. VIPSeg DataSet: A large-scale VIdeo Panoptic Segmentation dataset

• CVPR 2022: Large-scale Video Panoptic Segmentation in the Wild: A Benchmark
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Dataset Search Engine

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