1.git clone https://github.com/hjimce/compress_yolo
2.cd compress_yolo
3.vim Makefile and setPRUNE=1
4.start prune tiny-yolo:
./darknet detector train cfg/coco.data cfg/tiny-yolo.cfg pretrain/tiny-yolo.weights -gpus 0
5、copy backup file trained weights and test:
./darknet detector test cfg/coco.data cfg/tiny-yolo-test.cfg pretrain/tiny-yolo_prune.weights data/dog.jpg
6、test mAP:
./darknet detector valid cfg/coco.data cfg/tiny-yolo-test.cfg pretrain/tiny-yolo_prune.weights
compress the coco_results.json in results file and commit to https://competitions.codalab.org/competitions/5181#participate-submit_results
实验结果:64M compress to 18M