54 lines
2.5 KiB
Markdown
54 lines
2.5 KiB
Markdown
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English | [简体中文](README_cn.md)
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# PaddleDetection applied for specific scenarios
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We provide some models implemented by PaddlePaddle to detect objects in specific scenarios, users can download the models and use them in these scenarios.
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| Task | Algorithm | Box AP | Download | Configs |
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|:---------------------|:---------:|:------:| :-------------------------------------------------------------------------------------: |:------:|
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| Vehicle Detection | YOLOv3 | 54.5 | [model](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.0/configs/vehicle/vehicle_yolov3_darknet.yml) |
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## Vehicle Detection
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One of major applications of vehichle detection is traffic monitoring. In this scenary, vehicles to be detected are mostly captured by the cameras mounted on top of traffic light columns.
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### 1. Network
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The network for detecting vehicles is YOLOv3, the backbone of which is Dacknet53.
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### 2. Configuration for training
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PaddleDetection provides users with a configuration file [yolov3_darknet53_270e_coco.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.0/configs/yolov3/yolov3_darknet53_270e_coco.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for vehicle detection:
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* num_classes: 6
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* anchors: [[8, 9], [10, 23], [19, 15], [23, 33], [40, 25], [54, 50], [101, 80], [139, 145], [253, 224]]
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* nms/nms_top_k: 400
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* nms/score_threshold: 0.005
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* dataset_dir: dataset/vehicle
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### 3. Accuracy
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The accuracy of the model trained and evaluated on our private data is shown as followed:
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AP at IoU=.50:.05:.95 is 0.545.
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AP at IoU=.50 is 0.764.
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### 4. Inference
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Users can employ the model to conduct the inference:
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```
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export CUDA_VISIBLE_DEVICES=0
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python -u tools/infer.py -c configs/vehicle/vehicle_yolov3_darknet.yml \
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-o weights=https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams \
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--infer_dir configs/vehicle/demo \
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--draw_threshold 0.2 \
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--output_dir configs/vehicle/demo/output
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```
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Some inference results are visualized below:
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