docs/quick_run.md

# Get Started

## 1.Installation

Step1. Install YOLOX.
```shell
git clone git@github.com:Megvii-BaseDetection/YOLOX.git
cd YOLOX
pip3 install -U pip && pip3 install -r requirements.txt
pip3 install -v -e .  # or  python3 setup.py develop
```
Step2. Install [pycocotools](https://github.com/cocodataset/cocoapi).

```shell
pip3 install cython; pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
```

## 2.Demo

Step1. Download a pretrained model from the benchmark table.

Step2. Use either -n or -f to specify your detector's config. For example:

```shell
python tools/demo.py image -n yolox-s -c /path/to/your/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu]
```
or
```shell
python tools/demo.py image -f exps/default/yolox_s.py -c /path/to/your/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu]
```
Demo for video:
```shell
python tools/demo.py video -n yolox-s -c /path/to/your/yolox_s.pth --path /path/to/your/video --conf 0.25 --nms 0.45 --tsize 640 --save_result --device [cpu/gpu]
```


## 3.Reproduce our results on COCO

Step1. Prepare COCO dataset
```shell
cd <YOLOX_HOME>
ln -s /path/to/your/COCO ./datasets/COCO
```

Step2. Reproduce our results on COCO by specifying -n:

```shell
python tools/train.py -n yolox-s -d 8 -b 64 --fp16 -o [--cache]
                         yolox-m
                         yolox-l
                         yolox-x
```
* -d: number of gpu devices
* -b: total batch size, the recommended number for -b is num-gpu * 8
* --fp16: mixed precision training
* --cache: caching imgs into RAM to accelarate training, which need large system RAM.

**Weights & Biases for Logging**

To use W&B for logging, install wandb in your environment and log in to your W&B account using

```shell
pip install wandb
wandb login
```

Log in to your W&B account

To start logging metrics to W&B during training add the flag `--logger` to the previous command and use the prefix "wandb-" to specify arguments for initializing the wandb run.

```shell
python tools/train.py -n yolox-s -d 8 -b 64 --fp16 -o [--cache] --logger wandb wandb-project <project name>
                         yolox-m
                         yolox-l
                         yolox-x
```

More WandbLogger arguments include

```shell
python tools/train.py .... --logger wandb wandb-project <project-name> \
                wandb-name <run-name> \
                wandb-id <run-id> \
                wandb-save_dir <save-dir> \
                wandb-num_eval_images <num-images> \
                wandb-log_checkpoints <bool>
```

More information available [here](https://docs.wandb.ai/guides/integrations/other/yolox).

**Multi Machine Training**

We also support multi-nodes training. Just add the following args:
* --num\_machines: num of your total training nodes
* --machine\_rank: specify the rank of each node

When using -f, the above commands are equivalent to:

```shell
python tools/train.py -f exps/default/yolox-s.py -d 8 -b 64 --fp16 -o [--cache]
                         exps/default/yolox-m.py
                         exps/default/yolox-l.py
                         exps/default/yolox-x.py
```

## 4.Evaluation

We support batch testing for fast evaluation:

```shell
python tools/eval.py -n  yolox-s -c yolox_s.pth -b 64 -d 8 --conf 0.001 [--fp16] [--fuse]
                         yolox-m
                         yolox-l
                         yolox-x
```
* --fuse: fuse conv and bn
* -d: number of GPUs used for evaluation. DEFAULT: All GPUs available will be used.
* -b: total batch size across on all GPUs

To reproduce speed test, we use the following command:
```shell
python tools/eval.py -n  yolox-s -c yolox_s.pth -b 1 -d 1 --conf 0.001 --fp16 --fuse
                         yolox-m
                         yolox-l
                         yolox-x
```