如何训练LightWeight OpenPose

前期准备：

1　下载ＣＯＣＯ２０１７数据集，数据集地址为：http://cocodataset.org/#download](http://cocodataset.org/#download

解压至文件夹

２　安装要求的环境　　pip install -r requirements.txt
训练

训练包含三个步骤

１．训练MobileNet权重，经过此轮训练期望ＡＰ值达到３８％

２．训练上一阶段的权重，期待达到ＡＰ３９％

３训练上一阶段的权重，期待达到ＡＰ４０％

具体步骤：

１．下载MobileNet v1权重　mobilenet_sgd_68.848.pth.tar
２．　转换标签格式

python scripts/prepare_train_labels.py --labels /annotations/person_keypoints_train2017.json

产生prepared_train_annotation.pkl

３．python train.py --train-images-folder /train2017/ --prepared-train-labels prepared_train_annotation.pkl --val-labels val_subset.json --val-images-folder /val2017/ --checkpoint-path /mobilenet_sgd_68.848.pth.tar --from-mobilenet

4. python train.py --train-images-folder /train2017/ --prepared-train-labels prepared_train_annotation.pkl --val-labels val_subset.json --val-images-folder /val2017/ --checkpoint-path /checkpoint_iter_420000.pth --weights-only

5. 继续训练，三步调优, python train.py --train-images-folder /train2017/ --prepared-train-labels prepared_train_annotation.pkl --val-labels val_subset.json --val-images-folder /val2017/ --checkpoint-path /checkpoint_iter_280000.pth --weights-only --num-refinement-stages 3　 采用 checkpoint 370000 迭代后作为最终的结果．

We did not perform the best checkpoint selection at any step, so similar result may be achieved after less number of iterations.