更换文档检测模型

paddle_detection/configs/rotate/fcosr/README_en.md

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+English | [简体中文](README.md)
+
+# FCOSR
+
+## Content
+- [Introduction](#Introduction)
+- [Model Zoo](#Model-Zoo)
+- [Getting Start](#Getting-Start)
+- [Deployment](#Deployment)
+- [Citations](#Citations)
+
+## Introduction
+
+[FCOSR](https://arxiv.org/abs/2111.10780) is one stage anchor-free model based on [FCOS](https://arxiv.org/abs/1904.01355). FCOSR focuses on the label assignment strategy for oriented bounding boxes and proposes ellipse center sampling method and fuzzy sample assignment strategy. In terms of loss, FCOSR uses [ProbIoU](https://arxiv.org/abs/2106.06072) to avoid boundary discontinuity problem.
+
+## Model Zoo
+
+| Model | Backbone | mAP | Lr Scheduler | Angle | Aug | GPU Number | images/GPU | download | config |
+|:---:|:--------:|:----:|:---------:|:-----:|:--------:|:-----:|:------------:|:-------:|:------:|
+| FCOSR-M | ResNeXt-50 | 76.62 | 3x | oc | RR | 4 | 4 | [model](https://paddledet.bj.bcebos.com/models/fcosr_x50_3x_dota.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/fcosr/fcosr_x50_3x_dota.yml) |
+
+**Notes:**
+
+- if **GPU number** or **mini-batch size** is changed, **learning rate** should be adjusted according to the formula **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)**.
+- Models in model zoo is trained and tested with single scale by default. If `MS` is indicated in the data augmentation column, it means that multi-scale training and multi-scale testing are used. If `RR` is indicated in the data augmentation column, it means that RandomRotate data augmentation is used for training.
+
+## Getting Start
+
+Refer to [Data-Preparation](../README_en.md#Data-Preparation) to prepare data.
+
+### Training
+
+Single GPU Training
+``` bash
+CUDA_VISIBLE_DEVICES=0 python tools/train.py -c configs/rotate/fcosr/fcosr_x50_3x_dota.yml
+```
+
+Multiple GPUs Training
+``` bash
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/rotate/fcosr/fcosr_x50_3x_dota.yml
+```
+
+### Inference
+
+Run the follow command to infer single image, the result of inference will be saved in `output` directory by default.
+
+``` bash
+python tools/infer.py -c configs/rotate/fcosr/fcosr_x50_3x_dota.yml -o weights=https://paddledet.bj.bcebos.com/models/fcosr_x50_3x_dota.pdparams --infer_img=demo/P0861__1.0__1154___824.png --draw_threshold=0.5
+```
+
+### Evaluation on DOTA Dataset
+Refering to [DOTA Task](https://captain-whu.github.io/DOTA/tasks.html), You need to submit a zip file containing results for all test images for evaluation. The detection results of each category are stored in a txt file, each line of which is in the following format
+`image_id score x1 y1 x2 y2 x3 y3 x4 y4`. To evaluate, you should submit the generated zip file to the Task1 of [DOTA Evaluation](https://captain-whu.github.io/DOTA/evaluation.html). You can run the following command to get the inference results of test dataset:
+``` bash
+python tools/infer.py -c configs/rotate/fcosr/fcosr_x50_3x_dota.yml -o weights=https://paddledet.bj.bcebos.com/models/fcosr_x50_3x_dota.pdparams --infer_dir=/path/to/test/images --output_dir=output_fcosr --visualize=False --save_results=True
+```
+Process the prediction results into the format required for the official website evaluation:
+``` bash
+python configs/rotate/tools/generate_result.py --pred_txt_dir=output_fcosr/ --output_dir=submit/ --data_type=dota10
+
+zip -r submit.zip submit
+```
+
+## Deployment
+
+Please refer to the deployment tutorial[Deployment](../../../deploy/README_en.md)
+
+## Citations
+
+```
+@article{li2021fcosr,
+  title={Fcosr: A simple anchor-free rotated detector for aerial object detection},
+  author={Li, Zhonghua and Hou, Biao and Wu, Zitong and Jiao, Licheng and Ren, Bo and Yang, Chen},
+  journal={arXiv preprint arXiv:2111.10780},
+  year={2021}
+}
+
+@inproceedings{tian2019fcos,
+  title={Fcos: Fully convolutional one-stage object detection},
+  author={Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
+  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
+  pages={9627--9636},
+  year={2019}
+}
+
+@article{llerena2021gaussian,
+  title={Gaussian Bounding Boxes and Probabilistic Intersection-over-Union for Object Detection},
+  author={Llerena, Jeffri M and Zeni, Luis Felipe and Kristen, Lucas N and Jung, Claudio},
+  journal={arXiv preprint arXiv:2106.06072},
+  year={2021}
+}
+```