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VEDAI

VEDAI dataset prepared for darknet implementation

All images converted to .jpg
All annotations are standartized to <object-class> <x> <y> <width> <height>, where:

  • <object-class> - integer number of object from 0 to (classes-1)
  • <x> <y> <width> <height> - float values relative to width and height of image, it can be equal from 0.0 to 1.0
  • for example: <x> = <absolute_x> / <image_width> or <height> = <absolute_height> / <image_height>
  • atention: <x> <y> - are center of rectangle (are not top-left corner)

Classes:

0 - car  
1 - truck  
2 - pickup  
3 - tractor  
4 - camping car  
5 - boat  
6 - motorcycle  
7 - bus  
8 - van  
9 - other  
10 - small  
11 - large  

List of excluded images

You can find it in /unmarked

00000024.jpg  
00000039.jpg  
00000522.jpg  
00000606.jpg  
00000887.jpg  
00001185.jpg  
00000028.jpg  
00000424.jpg  
00000560.jpg  
00000717.jpg  
00001143.jpg  
00001244.jpg  
00000034.jpg  
00000425.jpg  
00000600.jpg  
00000878.jpg  
00001145.jpg  
00001248.jpg

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vedai dataset for darknet

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  • Python 100.0%