surveillance_monitoring/dependence/PaddleDetection/static/docs/tutorials/QUICK_STARTED.md

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Quick Start

In order to enable users to quickly produce models in a short time and master the use of PaddleDetection, this tutorial uses a pre-trained detection model to finetune small datasets. A good model can be produced in a short period of time. In actual business, it is recommended that users select a suitable model configuration file for adaptation according to their needs.

• Set GPU

  export CUDA_VISIBLE_DEVICES=0
  

  Quick Start

  ```

predict an image using PP-YOLO

python tools/infer.py -c configs/ppyolo/ppyolo.yml -o use_gpu=true weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams --infer_img=demo/000000014439.jpg

the result：

![](../images/000000014439.jpg)


## Prepare Dataset
The Dataset is [Kaggle dataset](https://www.kaggle.com/andrewmvd/road-sign-detection) ，Contains 877 images, 4 data categories: crosswalk, speedlimit, stop, trafficlight.
The dataset is divided into training set(contains 701 images) and test set(contains 176 images)，[download link](https://paddlemodels.bj.bcebos.com/object_detection/roadsign_voc.tar).

# python dataset/roadsign_voc/download_roadsign_voc.py

## Train、Eval、Infer
### 1、Train

It will takes about 5 minutes on GPU

-c set configt file

-o overwrite the settings in the configuration file

--eval Evaluate while training, and a model named best_model.pdmodel with the most evaluation results will be automatically saved

python tools/train.py -c configs/yolov3_mobilenet_v1_roadsign.yml --eval -o use_gpu=true

If you want to observe the loss change curve in real time through VisualDL, add --use_vdl=true to the training command, and set the log save path through --vdl_log_dir.
**Note: VisualDL need Python>=3.5**

Please install [VisualDL](https://github.com/PaddlePaddle/VisualDL) first

python -m pip install visualdl -i https://mirror.baidu.com/pypi/simple

python -u tools/train.py -c configs/yolov3_mobilenet_v1_roadsign.yml

                    --use_vdl=true \
                    --vdl_log_dir=vdl_dir/scalar \
                    --eval

View the change curve in real time through the visualdl command:

visualdl --logdir vdl_dir/scalar/ --host --port

### 2、Eval

Eval using best_model by default

-c set config file

-o overwrite the settings in the configuration file

CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/yolov3_mobilenet_v1_roadsign.yml -o use_gpu=true

### 3、Infer

-c set config file

-o overwrite the settings in the configuration file

--infer_img image path

After the prediction is over, an image of the same name with the prediction result will be generated in the output folder

python tools/infer.py -c configs/yolov3_mobilenet_v1_roadsign.yml -o use_gpu=true --infer_img=demo/road554.png ```

The result is as shown below：