更换文档检测模型

paddle_detection/configs/ppvehicle/vehicle_yolov3/README.md

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+English | [简体中文](README_cn.md)
+# PaddleDetection applied for specific scenarios
+
+We provide some models implemented by PaddlePaddle to detect objects in specific scenarios, users can download the models and use them in these scenarios.
+
+| Task                 | Algorithm | Box AP | Download                                                                                | Configs |
+|:---------------------|:---------:|:------:| :-------------------------------------------------------------------------------------: |:------:|
+| Vehicle Detection    |  YOLOv3  |  54.5  | [model](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [config](./vehicle_yolov3_darknet.yml) |
+
+## Vehicle Detection
+
+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.
+
+### 1. Network
+
+The network for detecting vehicles is YOLOv3, the backbone of which is Dacknet53.
+
+### 2. Configuration for training
+
+PaddleDetection provides users with a configuration file [yolov3_darknet53_270e_coco.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/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:
+
+* num_classes: 6
+* anchors: [[8, 9], [10, 23], [19, 15], [23, 33], [40, 25], [54, 50], [101, 80], [139, 145], [253, 224]]
+* nms/nms_top_k: 400
+* nms/score_threshold: 0.005
+* dataset_dir: dataset/vehicle
+
+### 3. Accuracy
+
+The accuracy of the model trained and evaluated on our private data is shown as followed:
+
+AP at IoU=.50:.05:.95 is 0.545.
+
+AP at IoU=.50 is 0.764.
+
+### 4. Inference
+
+Users can employ the model to conduct the inference:
+
+```
+export CUDA_VISIBLE_DEVICES=0
+python -u tools/infer.py -c configs/ppvehicle/vehicle_yolov3/vehicle_yolov3_darknet.yml \
+                         -o weights=https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams \
+                         --infer_dir configs/ppvehicle/vehicle_yolov3/demo \
+                         --draw_threshold 0.2 \
+                         --output_dir configs/ppvehicle/vehicle_yolov3/demo/output
+```
+
+Some inference results are visualized below:
+
+![](../../../docs/images/VehicleDetection_001.jpeg)
+
+![](../../../docs/images/VehicleDetection_005.png)