Datasets for image classification and detection
last modified : 19-07-2019
Here are listed all the datasets that can be used for image classification. Each dataset should come with a small description of its size, what's in it and who provided it.
The UA-DETRAC Benchmark Suite
This dataset is both for multi-object detection and multi-object tracking.
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Contains:
- Vehicles: Cars, Bus, Van, Other
- Weather: cloudy, sunny, rainy, night
- Different level of occlusion
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Size:
- more than 140 thousand frames
- 8250 vehicles manually annotated
- 1.21 million labeled bounding boxes of objects
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Other details:
- location: 24 different locations at Beijing and Tianjin in China
- 10 hours of videos captured with a Cannon EOS 550D camera
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Article:
- Title: UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
- Authors: Longyin Wen, Dawei Du, Zhaowei Cai, Zhen Lei, Ming{-}Ching Chang, Honggang Qi, Jongwoo Lim, Ming{-}Hsuan Yang and Siwei Lyu
- Link: article
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Dataset: here
The KITTI Vision Benchmark Suite
This dataset is both for multi-object detection and multi-object tracking.
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Contains:
- Tracking: 8 classes but only 'Car' and 'Pedestrian' have enough instance according to the website
- Detection / 2D Objects: Unspecified on website, cars at least
- Detection / 3D Objects: Unspecified on website, cars at least
- Detection / bird's eye view: Cars (maybe)
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Size:
- Tracking: 21 training sequences and 29 test sequences
- Detection / 2D Objects: 7481 training images and 7518 test images
- Detection / 3D Objects: 7481 training images and 7518 test images
- Detection / bird's eye view: 7481 training images and 7518 test images for 80 256 labeled objects
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Article:
- Title: Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite
- Authors: Andreas Geiger and Philip Lenz and Raquel Urtasun
- Link: article
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Dataset: here
The Berkeley BBD100K
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Contains:
- Vehicles: Bus, Light, Sign, Person, Bike, Truck, Motor, Car, Train, Rider
- Weather: clear, partly cloudy, over-cast, rainy, snowy, foggy, dawn/dusk, daytime, night
- Different level of occlusion
- Segmentation
- Different scenes, such as: residential, highway, city, street, ...
- Lane marking
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Size:
- 100,000 HD video sequences of over 1,100-hour driving experience;
- 2D Bounding Boxes annotated on 100,000 images;
- Segmentation over 10,000 diverse images with pixel-level and rich instance-level annotations;
- Multiple types of lane marking annotations on 100,000 images.
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Other details:
- location: Different location in the USA, New York, Berkeley, San Francisco
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Article:
- Title: BDD100K: A Diverse Driving Video Database withScalable Annotation Tooling
- Authors: Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, Trevor Darrell
- Link: article
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Dataset: here
Cifar-10
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Contains:
- airplane
- automobile
- bird
- cat
- deer
- dog
- frog
- horse
- ship
- truck
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Size:
- 60000 images divided into 6 batches with one for the tests
- images of size 32x32
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Dataset: here
VKitti
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Contains:
- Based on the Kitti dataset, should contain the same classes
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Size:
- 50 high-resolution monocular videos (21,260 frames)
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Other details:
- five different virtual worlds in urban settings under different imaging and weather conditions
- These photo-realistic synthetic videos are automatically, exactly, and fully annotated for 2D and 3D multi-object tracking and at the pixel level with category, instance, flow, and depth labels
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Article:
- Authors: Gaidon A, Wang Q, Cabon Y and Vig E
- Link: article
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Dataset: here