PyTorch-Spiking-YOLOv3:Spiking-YOLOv3的PyTorch实现。 根据YOLOv3的两个常见Py...

# PyTorch-Spiking-YOLOv3 A PyTorch implementation of Spiking-YOLOv3, based on the PyTorch implementation of YOLOv3([ultralytics/yolov3](https://github.com/ultralytics/yolov3)), with support for Spiking-YOLOv3-Tiny at present. The whole Spiking-YOLOv3 will be supported soon. ## Introduction For spiking implementation, some operators in YOLOv3-Tiny have been converted equivalently. Please refer to yolov3-tiny-ours(\*).cfg in /cfg for details. ### Conversion of some operators + 'maxpool(stride=2)'->'convolutional(stride=2)' + 'maxpool(stride=1)'->'none' + 'upsample'->'transposed_convolutional' + 'leaky_relu'->'relu' + 'batch_normalization'->'fuse_conv_and_bn' ## Usage Please refer to [ultralytics/yolov3](https://github.com/ultralytics/yolov3) for the basic usage for training, evaluation and inference. The main advantage of PyTorch-Spiking-YOLOv3 is the transformation from ANN to SNN. ### Train ``` $ python3 train.py --batch-size 32 --cfg cfg/yolov3-tiny-ours.cfg --data data/coco.data --weights '' ``` ### Test ``` $ python3 test.py --cfg cfg/yolov3-tiny-ours.cfg --data data/coco.data --weights weights/best.pt --batch-size 32 --img-size 640 ``` ### Detect ``` $ python3 detect.py --cfg cfg/yolov3-tiny-ours.cfg --weights weights/best.pt --img-size 640 ``` ### Transform ``` $ python3 ann_to_snn.py --cfg cfg/yolov3-tiny-ours.cfg --data data/coco.data --weights weights/best.pt --timesteps 128 ``` For higher accuracy(mAP), you can try to adjust some hyperparameters. *Trick: the larger timesteps, the higher accuracy.* ## Results Here we show the results(mAP) of PASCAL VOC & COCO which are commonly used in object detection，and two custom datasets UAV & UAVCUT. | dataset | yolov3 | yolov3-tiny | yolov3-tiny-ours | yolov3-tiny-ours-snn | | ---- | ---- | ---- | ---- | ---- | | UAVCUT | 98.90% | 99.10% | **98.80%** | **98.60%** | | UAV | 99.50% | 99.40% | **99.10%** | **98.20%** | | VOC07+12 | 77.00% | 52.30% | **55.50%** | **55.56%** | | COCO2014 | 56.50% | 33.30% | **38.70%** | **29.50%** | From the results, we can conclude that: 1) for simple custom datasets like UAV & UAVCUT, the accuracy of converting some operators is nearly equivalent to the original YOLOv3-Tiny; 2) for complex common datasets like PASCAL VOC & COCO, the accuracy of converting some operators is even better than the original YOLOv3-Tiny; 3) for most datasets, our method of transformation from ANN to SNN can be nearly lossless; 4) for rather complex dataset like COCO, our method of transformation from ANN to SNN causes a certain loss of accuracy(which will been improved later). UAVCUT  UAV  PASCAL VOC  COCO  ## References ### Articles + [Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks](https://arxiv.org/abs/1612.04052) + [Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection](https://arxiv.org/abs/1903.06530) ### GitHub + [NeuromorphicProcessorProject/snn_toolbox](https://github.com/NeuromorphicProcessorProject/snn_toolbox) + [hahnyuan/ANN2SNN](http://git.wildz.cn/hahnyuan/ANN2SNN)