PaddleDetection/configs/sniper

• SNIPER: Efficient Multi-Scale Training
• • Model Zoo
  • • Note
  • Getting Start
  • • 1. Training
    • 2. Evaluation
    • 3. Inference
  • Citations

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SNIPER: Efficient Multi-Scale Training

Model Zoo

Sniper	GPU number	images/GPU	Model	Dataset	Schedulers	Box AP	Download	Config
w/o	4	1	ResNet-r50-FPN	VisDrone	1x	23.3	Download Link	config
w/	4	1	ResNet-r50-FPN	VisDrone	1x	29.7	Download Link	config

Note

Here, we use VisDrone dataset, and to detect 9 objects including person, bicycles, car, van, truck, tricyle, awning-tricyle, bus, motor.

Getting Start

1. Training

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

python tools/sniper_params_stats.py FasterRCNN annotations/instances_train2017.json

b. optional: trian detector to get negative proposals.

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. train models

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. Evaluation

Evaluating SNIPER on custom dataset in single GPU with following commands:

# use saved checkpoint in training
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. Inference

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.

# inference single image
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

# inference all images in the directory
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}}