This task aims to predict the semantic part segmentation map and instance-level human parsing map of an image which contains multiple persons.
To stimulate the multiple-human parsing research, we collect the images with multiple person instances to establish the first standard and comprehensive benchmark for instance-level human parsing. Our Crowd Instance-level Human Parsing Dataset (CIHP) contains 28280 training, 5000 validation and 5000 test images, in which there are 38280 multiple-person images in total.
For this task, we use two metrics for multi-human parsing evaluation. The final score is the average of the results of these two metrics.
The results should be pack into a single zip file. Example zip file is available in mp_results.zip.
Specifically, the zip file contains two 2 sub-folders in it:
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.
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.
After uploading you results, please wait for 40 to 50 minutes and refrash your page to see the scores.
The datasets are released for academic research only and it is free to researchers from educational or research institutions for non-commercial purposes. When downloading the dataset you agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
For more information, please concate us at email@example.com or firstname.lastname@example.org.
Start: Feb. 20, 2020, midnight
Sept. 30, 2020, midnight
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