gjylt / ai-challenger-keypoints-pytorch Goto Github PK

Codes for reproducing Associative Embedding: End-to-End Learning for Joint Detection and Grouping

 

1. src: source codes including model, data reader and utils
2. tools: main functions to run testing or visualizing
3. scripts: scripts to run testing or visualizing

Other directories are self-explanatory

• Python2.7
• OpenCV
• Pytorch v0.3.0
• CUDNN
• numpy

Pretrained model is available here. Include the model in the ./checkpoints directory or modify the variable CHECKPOINT in ./scripts/test.sh.

Run

./scripts/init_dir.sh

to make necessary directories.

Run

./scripts/test.sh

to test model on images in media directory. Or you may change the variable IMAGES_DIR in ./scripts/test.sh to test on your own images.

In the same way, run

./scripts/visualize.sh

to visualize results. The rendered images will be save at ./results/imgs

In the JSON format, results are as follow

[
    {
        "image_id": "a0f6bdc065a602b7b84a67fb8d14ce403d902e0d",
        "keypoint_annotations": {
        "human1": [261, 294, 1, 281, 328, 1, 0, 0, 0, 213, 295, 1, 208, 346, 1, 192, 335, 1, 245, 375, 1, 255, 432, 1, 244, 494, 1, 221, 379, 1, 219, 442, 1, 226, 491, 1, 226, 256, 1, 231, 284, 1],
        "human2": [313, 301, 1, 305, 337, 1, 321, 345, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 313, 359, 1, 320, 409, 1, 311, 454, 1, 0, 0, 0, 330, 409, 1, 324, 446, 1, 337, 284, 1, 327, 302, 1],
        "human3": [373, 304, 1, 346, 286, 1, 332, 263, 1, 0, 0, 0, 0, 0, 0, 345, 313, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 363, 386, 1, 361, 424, 1, 361, 475, 1, 365, 273, 1, 369, 297, 1],
        }
    }
]

Detailed explanation could be found in the website AI Challenger

• The time to release training codes is not decided yet.
• The model is simplified due to GPU memory limitation. The output feature map is 8 times smaller than the input image instead.