1. Download CityPersons coco2014 CrowdHuman CUHKocclusion ETHZ VOCdevkit dataset to train.

2. convert annotations as follow: retina-train.txt content:

   G:\datasets\pedestrian\CityPersons\images\train\aachen_000000_000019_leftImg8bit.png

   892 445 913 498 #lefttop point right bottom point 901 443 935 498 1844 436 1888 542

   G:\datasets\pedestrian\CityPersons\images\train\aachen_000002_000019_leftImg8bit.png

   1290 425 1315 486

$ train.py [-h] [data_path DATA_PATH] [--batch BATCH] [--epochs EPOCHS] [--shuffle SHUFFLE] [img_size IMG_SIZE] [--verbose VERBOSE] [--save_step SAVE_STEP] [--eval_step EVAL_STEP] [--save_path SAVE_PATH] [--depth DEPTH]

For multi-gpus training, run:

$ CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train.py

missing due to other reason

the improvement of AP is little compare to face detect. the reason maybe:

1. the sample has more mistake in label. so focal loss will learn more bad thing.
2. pedestrian box has more background and shield. the detection is more difficult.

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refrence: https://github.com/supernotman/RetinaFace_Pytorch.git