Characterized Crowd Instance-level Human Parsing (CCIHP) dataset

CCIHP dataset provides pixelwise image annotations for:

human segmentation,
semantic attribute segmentation and
semantic attribute characterization.

Data were annotated with the open-source tool pixano.

Dataset description

Images:
The image data are the same as CIHP dataset (see Section Related work) proposed at the LIP (Look Into Person) challenge. They are available at google drive and baidu drive. (Baidu link does not need access right).
Annotations:
The CCIHP annotations can be found in the Training and Validation sub-folders of CCIHP_icip2021/dataset/ folder. Annotations consist of:
- Human_ids: person instance labels
- Instance_ids: attribute instance labels
- Category_ids: attribute category labels
- Size_ids: size category labels
- Pattern_ids: pattern category labels
- Color_ids: color category labels

Label meaning for semantic attribute/body parts:

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

Label meaning for size characterization:

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

Label meaning for pattern characterization:

Solid
Geometrical: Stripes, Checks, Dots…
Fancy: Flowers, Military…
Letters: Letters, numbers, symbols…

Label meaning for color characterization:

Dark: No dominant color, includes black, navy blue
Medium: No dominant color, including gray
Light: No dominant color, including white
Brown
Red
Pink
Yellow
Orange
Green
Blue
Purple
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.

Evaluation

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

Requirements

python==3.6+
opencv-python
pillow

Evaluation steps

Run generate_characteristic_instance_part_ccihp.py
Run eval_test_characteristic_inst_part_ap_ccihp.py 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.
Run metric_ccihp_miou_evaluation.py for a mIoU performance evaluation of semantic predictions (attribute or characteristics).

License

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

Evaluation codes are under MIT license.

Citation

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.

@INPROCEEDINGS{ccihp_dataset_2021,
  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}, 
  year={2021},
  volume={},
  number={},
  pages={2528-2532},
  doi={10.1109/ICIP42928.2021.9506509}},

Contact

If you have any question about this dataset, you can contact us by email at: ccihp-dataset@cea.fr