mirror of https://github.com/JDAI-CV/fast-reid.git
43 lines
1.5 KiB
Markdown
43 lines
1.5 KiB
Markdown
# Getting Started with Fastreid
|
|
|
|
## Prepare pretrained model
|
|
|
|
If you use backbones supported by fastreid, you do not need to do anything. It will automatically download the pre-train models.
|
|
But if your network is not connected, you can download pre-train models manually and put it in `~/.cache/torch/checkpoints`.
|
|
|
|
If you want to use other pre-train models, such as MoCo pre-train, you can download by yourself and set the pre-train model path in `configs/Base-bagtricks.yml`.
|
|
|
|
## Compile with cython to accelerate evalution
|
|
|
|
```bash
|
|
cd fastreid/evaluation/rank_cylib; make all
|
|
```
|
|
|
|
## Training & Evaluation in Command Line
|
|
|
|
We provide a script in "tools/train_net.py", that is made to train all the configs provided in fastreid.
|
|
You may want to use it as a reference to write your own training script.
|
|
|
|
To train a model with "train_net.py", first setup up the corresponding datasets following [datasets/README.md](https://github.com/JDAI-CV/fast-reid/tree/master/datasets), then run:
|
|
|
|
```bash
|
|
./tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml MODEL.DEVICE "cuda:0"
|
|
```
|
|
|
|
The configs are made for 1-GPU training.
|
|
|
|
If you want to train model with 4 GPUs, you can run:
|
|
|
|
```bash
|
|
python tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml --num-gpus 4
|
|
```
|
|
|
|
To evaluate a model's performance, use
|
|
|
|
```bash
|
|
python tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml --eval-only \
|
|
MODEL.WEIGHTS /path/to/checkpoint_file MODEL.DEVICE "cuda:0"
|
|
```
|
|
|
|
For more options, see `./tools/train_net.py -h`.
|