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+# 训练模型：
+先配置好conda环境后，
+安装
+```
+python -m pip install detectron2 -f \
+  https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.8/index.html
+```
+以及pytorch
+然后拉去代码库中的yolof
+Install YOLOF by:
+```
+python setup.py develop
+```
+Then link your dataset path to datasets
+```
+cd datasets/
+ln -s /path/to/coco coco
+```
+Download the pretrained model in OneDrive or in the Baidu Cloud with code qr6o to train with the CSPDarkNet-53 backbone (optional)
+```
+mkdir pretrained_models
+# download the `cspdarknet53.pth` to the `pretrained_models` directory
+```
+Train with yolof
+```
+python ./tools/train_net.py --num-gpus 8 --config-file ./configs/yolof_R_50_C5_1x.yaml
+```
+Test with yolof
+```
+python ./tools/train_net.py --num-gpus 8 --config-file ./configs/yolof_R_50_C5_1x.yaml --eval-only MODEL.WEIGHTS /path/to/checkpoint_file
+```
+Note that there might be API changes in future detectron2 releases that make the code incompatible.
+# 生成onnx
+需要先拉取代码库里的detectron2
+然后执行
+```
+python -m pip install -e detectron2
+```
+
+然后执行
+```
+python ~/detectron2/tools/deploy/export_model.py --format onnx --export-method caffe2_tracing --config-file ~/yoloF_test/YOLOF/configs/yolof_X_101_64x4d_C5_1x.yaml --output ./output3 MODEL.DEVICE cuda MODEL.WEIGHTS /root/yoloF_test/YOLOF/pretrained_models/YOLOF_X_101_64x4d_C5_1x.pth
+```
+这样会在output3 底下生成一个onnx文件
+
+# 使用tensorrt
+
+先进入docker
+```
+nvidia-docker run -it --rm --network=host -v /root:/root nvcr.io/nvidia/pytorch:20.12-py3 /bin/bash
+```
+然后使用conda切换到pytorch虚拟环境
+
+然后拉取yolof_tensorrt
+进入yolof_tensorrt文件夹后，执行以下命令
+```
+python main.py
+```