PulseFocusPlatform/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 data sets. 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：

Prepare Dataset

The Dataset is Kaggle dataset ，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.

#
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 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 <host_IP> --port <port_num>

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：