更换文档检测模型

2024-08-27 14:42:45 +08:00
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+简体中文 | [English](README.md)
+
+# SNIPER: Efficient Multi-Scale Training
+
+## 模型库
+| 有无sniper   | GPU个数    | 每张GPU图片个数 | 骨架网络             | 数据集     | 学习率策略 | Box AP |                           模型下载                          | 配置文件 |
+| :---------------- | :-------------------: | :------------------: | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
+| w/o sniper   |    4    |    1    | ResNet-r50-FPN      | [VisDrone](https://github.com/VisDrone/VisDrone-Dataset)  |   1x    |  23.3  | [下载链接](https://bj.bcebos.com/v1/paddledet/models/faster_rcnn_r50_fpn_1x_visdrone.pdparams ) | [配置文件](./faster_rcnn_r50_fpn_1x_visdrone.yml) |
+| w sniper |    4    |    1    | ResNet-r50-FPN      | [VisDrone](https://github.com/VisDrone/VisDrone-Dataset)    |   1x    |  29.7  | [下载链接](https://bj.bcebos.com/v1/paddledet/models/faster_rcnn_r50_fpn_1x_sniper_visdrone.pdparams) | [配置文件](./faster_rcnn_r50_fpn_1x_sniper_visdrone.yml) |
+
+
+### 注意
+- 我们使用的是`VisDrone`数据集, 并且检查其中的9类，包括 `person, bicycles, car, van, truck, tricyle, awning-tricyle, bus, motor`.
+- 暂时不支持和导出预测部署（deploy).
+
+
+## 使用说明
+### 1. 训练
+a. 可选：统计数据集信息，获得数据缩放尺度、有效框范围、chip尺度和步长等参数，修改configs/datasets/sniper_coco_detection.yml中对应参数
+```bash
+python tools/sniper_params_stats.py FasterRCNN annotations/instances_train2017.json
+```
+b. 可选：训练检测器，生成负样本
+```bash
+python -m paddle.distributed.launch --log_dir=./sniper/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/sniper/faster_rcnn_r50_fpn_1x_sniper_visdrone.yml --save_proposals --proposals_path=./proposals.json &>sniper.log 2>&1 &
+```
+c. 训练模型
+```bash
+python -m paddle.distributed.launch --log_dir=./sniper/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/sniper/faster_rcnn_r50_fpn_1x_sniper_visdrone.yml --eval &>sniper.log 2>&1 &
+```
+
+### 2. 评估
+使用单GPU通过如下命令一键式评估模型在COCO val2017数据集效果
+```bash
+# 使用训练保存的checkpoint
+CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/sniper/faster_rcnn_r50_fpn_1x_sniper_visdrone.yml -o weights=output/faster_rcnn_r50_fpn_1x_sniper_visdrone/model_final
+```
+
+### 3. 推理
+使用单GPU通过如下命令一键式推理图像，通过`--infer_img`指定图像路径，或通过`--infer_dir`指定目录并推理目录下所有图像
+
+```bash
+# 推理单张图像
+CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/sniper/faster_rcnn_r50_fpn_1x_sniper_visdrone.yml -o weights=output/faster_rcnn_r50_fpn_1x_sniper_visdrone/model_final --infer_img=demo/P0861__1.0__1154___824.png
+
+# 推理目录下所有图像
+CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/sniper/faster_rcnn_r50_fpn_1x_sniper_visdrone.yml -o weights=output/faster_rcnn_r50_fpn_1x_sniper_visdrone/model_final --infer_dir=demo
+```
+
+## Citations
+```
+@misc{1805.09300,
+Author = {Bharat Singh and Mahyar Najibi and Larry S. Davis},
+Title = {SNIPER: Efficient Multi-Scale Training},
+Year = {2018},
+Eprint = {arXiv:1805.09300},
+}
+
+@ARTICLE{9573394,
+  author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
+  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  title={Detection and Tracking Meet Drones Challenge},
+  year={2021},
+  volume={},
+  number={},
+  pages={1-1},
+  doi={10.1109/TPAMI.2021.3119563}}
+```