68 lines
3.5 KiB
Markdown
68 lines
3.5 KiB
Markdown
English | [简体中文](README_cn.md)
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# SNIPER: Efficient Multi-Scale Training
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## Model Zoo
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| Sniper | GPU number | images/GPU | Model | Dataset | Schedulers | Box AP | Download | Config |
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| :---------------- | :-------------------: | :------------------: | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
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| w/o | 4 | 1 | ResNet-r50-FPN | [VisDrone](https://github.com/VisDrone/VisDrone-Dataset) | 1x | 23.3 | [Download Link](https://bj.bcebos.com/v1/paddledet/models/faster_rcnn_r50_fpn_1x_visdrone.pdparams ) | [config](./faster_rcnn_r50_fpn_1x_visdrone.yml) |
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| w/ | 4 | 1 | ResNet-r50-FPN | [VisDrone](https://github.com/VisDrone/VisDrone-Dataset) | 1x | 29.7 | [Download Link](https://bj.bcebos.com/v1/paddledet/models/faster_rcnn_r50_fpn_1x_sniper_visdrone.pdparams) | [config](./faster_rcnn_r50_fpn_1x_sniper_visdrone.yml) |
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### Note
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- Here, we use VisDrone dataset, and to detect 9 objects including `person, bicycles, car, van, truck, tricycle, awning-tricycle, bus, motor`.
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- Do not support deploy by now because sniper dataset crop behavior.
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## Getting Start
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### 1. Training
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a. optional: Run `tools/sniper_params_stats.py` to get image_target_sizes\valid_box_ratio_ranges\chip_target_size\chip_target_stride，and modify this params in configs/datasets/sniper_coco_detection.yml
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```bash
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python tools/sniper_params_stats.py FasterRCNN annotations/instances_train2017.json
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```
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b. optional: train detector to get negative proposals.
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```bash
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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 &
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```
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c. train models
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```bash
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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 &
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```
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### 2. Evaluation
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Evaluating SNIPER on custom dataset in single GPU with following commands:
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```bash
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# use saved checkpoint in training
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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
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```
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### 3. Inference
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Inference images in single GPU with following commands, use `--infer_img` to inference a single image and `--infer_dir` to inference all images in the directory.
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```bash
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# inference single image
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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
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# inference all images in the directory
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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
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```
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## Citations
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```
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@misc{1805.09300,
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Author = {Bharat Singh and Mahyar Najibi and Larry S. Davis},
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Title = {SNIPER: Efficient Multi-Scale Training},
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Year = {2018},
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Eprint = {arXiv:1805.09300},
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}
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@ARTICLE{9573394,
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author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
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journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
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title={Detection and Tracking Meet Drones Challenge},
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year={2021},
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volume={},
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number={},
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pages={1-1},
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doi={10.1109/TPAMI.2021.3119563}}
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```
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