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# Few-shot YOLOv3: Few-shot Object Detection on Optical Remote Sensing Images Our code is based on [https://github.com/marvis/pytorch-yolo2](https://github.com/marvis/pytorch-yolo2) and developed with Python 2.7 & PyTorch 0.3.1. ## Detection Examples <div align=center> <img src="https://user-images.githubusercontent.com/47288017/85243067-5051d380-b473-11ea-9a8e-17e19692746c.png" width="740"> </div> <div align=center> Samples of novel class detection result on NWPU VHR-10 and DIOR with 10-shot training bounding boxes. </div> ## Model <div align=center> <img src="https://user-images.githubusercontent.com/47288017/85242838-ab36fb00-b472-11ea-9ec8-418d06d057a0.png" width="740"> </div> ## Training our model on NWPU VHR-10 - ``` $PROJ_ROOT : project root ``` - ``` $DATA_ROOT : dataset root ``` ### Prepare dataset Get the NWPU VHR-10 data from https://1drv.ms/u/s!AmgKYzARBl5cczaUNysmiFRH4eE Preprocess data ``` cd $PROJ_ROOT python scripts/ImgSplit_nwpu.py $DATA_ROOT ``` Generate labels for NWPU VHR-10 ``` python scripts/label_nwpu.py $DATA_ROOT ``` Generate per-class labels for NWPU VHR-10 (used for the Reweighting Module input) ``` python scripts/label_1c_nwpu.py $DATA_ROOT ``` Generate few-shot datasets Change the ''DROOT'' varibale in scripts/gen_fewlist_nwpu.py to $DATA_ROOT ``` python scripts/gen_fewlist_nwpu.py ``` Generate training dictionary ``` python scripts/gen_dict_file.py $DATA_ROOT nwpu ``` ### Base Training Modify Config for NWPU VHR-10 Data Change the cfg/fewyolov3_nwpu.data file ``` metayolo = 1 metain_type = 2 data = nwpu neg = 1 rand = 0 novel = data/nwpu_novels.txt novelid = 0 learning_rate = 0.001 steps = -1,64000 scales = 1,0.1 meta = data/nwpu_traindict_full.txt train = $DATA_ROOT/training.txt valid = $DATA_ROOT/evaluation.txt backup = backup/fewyolov3_nwpu gpus = 0,1,2,3 ``` Train the Model ``` python train.py cfg/fewyolov3_nwpu.data cfg/darknet_yolov3_spp.cfg cfg/reweighting_net.cfg ``` Evaluate the Model ``` python valid.py cfg/fewyolov3_nwpu.data cfg/darknet_yolov3_spp.cfg cfg/reweighting_net.cfg path/toweightfile python scripts/voc_eval.py results/path/to/comp4_det_test_ cfg/metayolo.data ``` ### Few-shot Tuning Modify Config for NWPU VHR-10 Data Change the cfg/fewtunev3_nwpu_10shot.data file (change the shot number to try different shots) ``` metayolo = 1 metain_type = 2 data = nwpu tuning = 1 neg = 0 rand = 0 novel = data/nwpu_novels.txt novelid = 0 max_epoch = 2000 repeat = 200 dynamic = 0 scale = 1 learning_rate = 0.0001 steps = -1,64000 scales = 1,0.1 train = $DATA_ROOT/training.txt meta = data/nwpu_traindict_bbox_10shot.txt valid = $DATA_ROOT/evaluation.txt backup = backup/fewtunetestv3_nwpu_10shot gpus = 0,1,2,3 ``` Train the Model with 10 shot ``` python train.py cfg/fewtunev3_nwpu_10shot.data cfg/darknet_yolov3_spp.cfg cfg/reweighting_net.cfg path/to/base/weightfile ``` Evaluate the Model ``` python valid.py cfg/fewtunev3_nwpu_10shot.data cfg/darknet_yolov3_spp.cfg cfg/reweighting_net.cfg path/to/tuned/weightfile python scripts/voc_eval.py results/path/to/comp4_det_test_ cfg/fewtunev3_nwpu_10shot.data ``` ## Acknowledgements Large part of the code is borrowed from [YOLO-Low-Shot](https://github.com/bingykang/Fewshot_Detection) ## Citation If you find this useful in your research, please consider citing: @article{deng2020few, title={Few-shot Object Detection on Remote Sensing Images}, author={Deng, Jingyu and Li, Xiang and Fang, Yi}, journal={arXiv preprint arXiv:2006.07826}, year={2020} }