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SNIPER: Efficient Multi-Scale Training
Model Zoo
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}}