PyTorch-Spiking-YOLOv3

A PyTorch implementation of Spiking-YOLOv3, based on the PyTorch implementation of YOLOv3(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 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
• Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection

GitHub

• NeuromorphicProcessorProject/snn_toolbox
• hahnyuan/ANN2SNN