forked from PulseFocusPlatform/PulseFocusPlatform
92 lines
3.2 KiB
Markdown
92 lines
3.2 KiB
Markdown
English | [简体中文](QUICK_STARTED_cn.md)
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# Quick Start
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In order to enable users to experience PaddleDetection and produce models in a short time, this tutorial introduces the pipeline to get a decent object detection model by finetuning on a small dataset in 10 minutes only. In practical applications, it is recommended that users select a suitable model configuration file for their specific demand.
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- **Set GPU**
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```bash
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export CUDA_VISIBLE_DEVICES=0
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```
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## Inference Demo with Pre-trained Models
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```
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# predict an image using PP-YOLO
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python tools/infer.py -c configs/ppyolo/ppyolo_r50vd_dcn_1x_coco.yml -o use_gpu=true weights=https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams --infer_img=demo/000000014439.jpg
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```
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the result：
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## Data preparation
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The Dataset is [Kaggle dataset](https://www.kaggle.com/andrewmvd/road-sign-detection) ，including 877 images and 4 data categories: crosswalk, speedlimit, stop, trafficlight. The dataset is divided into training set (701 images) and test set (176 images)，[download link](https://paddlemodels.bj.bcebos.com/object_detection/roadsign_voc.tar).
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```
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# Note: this command could skip and
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# the dataset will be dowloaded automatically at the stage of training.
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python dataset/roadsign_voc/download_roadsign_voc.py
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```
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## Training & Evaluation & Inference
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### 1、Training
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```
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# It will takes about 10 minutes on 1080Ti and 1 hour on CPU
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# -c set configuration file
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# -o overwrite the settings in the configuration file
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# --eval Evaluate while training, and a model named best_model.pdmodel with the most evaluation results will be automatically saved
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python tools/train.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml --eval -o use_gpu=true
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```
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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.
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**Note: VisualDL need Python>=3.5**
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Please install [VisualDL](https://github.com/PaddlePaddle/VisualDL) first
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```
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python -m pip install visualdl -i https://mirror.baidu.com/pypi/simple
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```
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```
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python -u tools/train.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml \
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--use_vdl=true \
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--vdl_log_dir=vdl_dir/scalar \
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--eval
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```
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View the change curve in real time through the visualdl command:
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```
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visualdl --logdir vdl_dir/scalar/ --host <host_IP> --port <port_num>
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```
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### 2、Evaluation
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```
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# Evaluate best_model by default
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# -c set config file
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# -o overwrite the settings in the configuration file
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python tools/eval.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml -o use_gpu=true
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```
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The final mAP should be around 0.85. The dataset is small so the precision may vary a little after each training.
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### 3、Inference
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```
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# -c set config file
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# -o overwrite the settings in the configuration file
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# --infer_img image path
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# After the prediction is over, an image of the same name with the prediction result will be generated in the output folder
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python tools/infer.py -c configs/yolov3/yolov3_mobilenet_v1_roadsign.yml -o use_gpu=true --infer_img=demo/road554.png
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```
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The result is as shown below：
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