一、安装环境
# install requirements pip install pycocotools numpy opencv-python tqdm tensorboard tensorboardX pyyaml pip install torch==1.4.0 pip install torchvision==0.5.0
二、下载pytorch版efficientdet源码
git clone https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch.git
源码链接：https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch
三、准备数据集
# your dataset structure should be like this datasets/ -your_project_name/ -train_set_name/ -*.jpg -val_set_name/ -*.jpg -annotations -instances_{train_set_name}.json -instances_{val_set_name}.json # for example, coco2017 datasets/ -coco2017/ -train2017/ -000000000001.jpg -000000000002.jpg -000000000003.jpg -val2017/ -000000000004.jpg -000000000005.jpg -000000000006.jpg -annotations -instances_train2017.json -instances_val2017.json
四、修改配置文件
# create a yml file {your_project_name}.yml under 'projects'folder
# modify it following 'coco.yml'
# for example
project_name: coco
train_set: train2017
val_set: val2017
num_gpus: 4 # 0 means using cpu, 1-N means using gpus
# mean and std in RGB order, actually this part should remain unchanged as long as your dataset is similar to coco.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
# this is coco anchors, change it if necessary
anchors_scales: '[2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)]'
anchors_ratios: '[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]'
# objects from all labels from your dataset with the order from your annotations.
# its index must match your dataset's category_id.
# category_id is one_indexed,
# for example, index of 'car' here is 2, while category_id of is 3
obj_list: ['person', 'bicycle', 'car', ...]
五、训练coco数据集
# train efficientdet-d1 on a custom dataset # with batchsize 8 and learning rate 1e-5 python train.py -c 1 -p your_project_name --batch_size 8 --lr 1e-5
六、训练带已训练好的权重的数据集
# train efficientdet-d2 on a custom dataset with pretrained weights # with batchsize 8 and learning rate 1e-5 for 10 epoches python train.py -c 2 -p your_project_name --batch_size 8 --lr 1e-5 --num_epochs 10 \ --load_weights /path/to/your/weights/efficientdet-d2.pth # with a coco-pretrained, you can even freeze the backbone and train heads only # to speed up training and help convergence. python train.py -c 2 -p your_project_name --batch_size 8 --lr 1e-5 --num_epochs 10 \ --load_weights /path/to/your/weights/efficientdet-d2.pth \ --head_only True
权重下载链接：https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch/releases/
七、尽早停止训练
# while training, press Ctrl+c, the program will catch KeyboardInterrupt # and stop training, save current checkpoint.
八、恢复训练
# let says you started a training session like this. python train.py -c 2 -p your_project_name --batch_size 8 --lr 1e-5 \ --load_weights /path/to/your/weights/efficientdet-d2.pth \ --head_only True # then you stopped it with a Ctrl+c, it exited with a checkpoint # now you want to resume training from the last checkpoint # simply set load_weights to 'last' python train.py -c 2 -p your_project_name --batch_size 8 --lr 1e-5 \ --load_weights last \ --head_only True
九、评估模型性能
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# eval on your_project, efficientdet-d5 python coco_eval.py -p your_project_name -c 5 \ -w /path/to/your/weights
十、调试训练（可选）
# when you get bad result, you need to debug the training result. python train.py -c 2 -p your_project_name --batch_size 8 --lr 1e-5 --debug True # then checkout test/ folder, there you can visualize the predicted boxes during training # don't panic if you see countless of error boxes, it happens when the training is at early stage. # But if you still can't see a normal box after several epoches, not even one in all image, # then it's possible that either the anchors config is inappropriate or the ground truth is corrupted.
十一、个人训练总结
最重要的是不放弃！！！
遇到错误，根据错误来源看代码，当然一般按照流程来不会出错。