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LIP: Multi-Person Human Parsing Challenge

Multi-Person Human Parsing Track

1. Metrics

For this task, we use two metrics for multi-human parsing evaluation. Mean IoU(%) for semantic part segmentation, reported by the FCN paper. And APr for instance-level human parsing, first introduced in Simultaneous Detection and Segmentation. The details show APr at various IoU thresholds.

2. Submit format

A parent folder named mp_results.zip(Click to download a template file) contains 2 sub-folders in it:

1. global_parsing

A folder of png images, named as "global_parsing". The content of id.png is the global human parsing results (instance-agnostic) for the image with exactly the same size.

2. instance_parsing

Named as "instance_parsing", this folder consist of two things:
   1) An indexed-png image with the segmentation. Here, each number belongs to a unique part. 0 is always assumed to be the background label.
   2) A text file. Each line is of the format < class_id score >. The first line of this file corresponds to 1 in the indexed png, the second line corresponds to 2 in the indexed png and so on.

3. Class Definition

  1. Background
  2. Hat
  3. Hair
  4. Glove
  5. Sunglasses
  6. Upper-clothes
  7. Dress
  8. Coat
  9. Socks
  10. Pants
  11. tosor-skin
  12. Scarf
  13. Skirt
  14. Face
  15. Left-arm
  16. Right-arm
  17. Left-leg
  18. Right-leg
  19. Left-shoe
  20. Right-shoe

4. Dataset Examples