Characterized Crowd Instance-level Human Parsing (CCIHP) dataset

CCIHP dataset provides pixelwise image annotations for:

Data were annotated with the open-source tool pixano.

Dataset description

Label meaning for semantic attribute/body parts:

  1. Hat: Hat, helmet, cap, hood, veil, headscarf, part covering the skull and hair of a hood/balaclava, crown…
  2. Hair
  3. Glove
  4. Sunglasses/Glasses: Sunglasses, eyewear, protective glasses…
  5. UpperClothes: T-shirt, shirt, tank top, sweater under a coat, top of a dress…
  6. Face Mask: Protective mask, surgical mask, carnival mask, facial part of a balaclava, visor of a helmet…
  7. Coat: Coat, jacket worn without anything on it, vest with nothing on it, a sweater with nothing on it…
  8. Socks
  9. Pants: Pants, shorts, tights, leggings, swimsuit bottoms… (clothing with 2 legs)
  10. Torso-skin
  11. Scarf: Scarf, bow tie, tie…
  12. Skirt: Skirt, kilt, bottom of a dress…
  13. Face
  14. Left-arm
  15. Right-arm
  16. Left-leg
  17. Right-leg
  18. Left-shoe
  19. Right-shoe
  20. Bag: Backpack, shoulder bag, fanny pack… (bag carried on oneself)
  21. Others: Jewelry, tags, bibs, belts, ribbons, pins, head decorations, headphones…

Label meaning for size characterization:

  1. Short: Small, short, narrow
  2. Long: Long, large, big
  3. Undetermined: If the attribute is partially hidden
  4. Sparse/bald: For hair attribute only

Label meaning for pattern characterization:

  1. Solid
  2. Geometrical: Stripes, Checks, Dots…
  3. Fancy: Flowers, Military…
  4. Letters: Letters, numbers, symbols…

Label meaning for color characterization:

  1. Dark: No dominant color, includes black, navy blue
  2. Medium: No dominant color, including gray
  3. Light: No dominant color, including white
  4. Brown
  5. Red
  6. Pink
  7. Yellow
  8. Orange
  9. Green
  10. Blue
  11. Purple
  12. Multicolor: When there is a pattern with several colors

Our work is based on CIHP image dataset from: Ke Gong, Xiaodan Liang, Yicheng Li, Yimin Chen, Ming Yang and Liang Lin, “Instance-level Human Parsing via Part Grouping Network”, ECCV 2018.


To evaluate the predictions given by a Human Parsing with Characteristics model, you can run the python scripts in CCIHP_icip2021/evaluation/ folder.


Evaluation steps

  1. Run
  2. Run for mean Average Precision based on characterized region (AP^(cr)_(vol)). It evaluates the prediction of characteristic (class & score) relative to each instanced and characterized attribute mask, independently of the attribute class prediction.
  3. Run for a mIoU performance evaluation of semantic predictions (attribute or characteristics).


Data annotations are under Creative Commons Attribution Non Commercial 4.0 license.

Evaluation codes are under MIT license.


A. Loesch and R. Audigier, “Describe Me If You Can! Characterized Instance-Level Human Parsing,” 2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 2528-2532, doi: 10.1109/ICIP42928.2021.9506509.

  author={Loesch, Angelique and Audigier, Romaric},
  booktitle={2021 IEEE International Conference on Image Processing (ICIP)}, 
  title={Describe Me If You Can! Characterized Instance-Level Human Parsing}, 


If you have any question about this dataset, you can contact us by email at: