This is the official implementation of the 'SFOD: Spiking Fusion Object Detector' .
Repository | Version |
---|---|
CUDA | 11.7 |
cuDNN | V8.0.0 |
Python | 3.9.0 |
Pytorch | 1.12.1 |
Torchvision | 0.13.1 |
Torchmetrics | 0.11.4 |
Pytorch-lightning | 2.0.1 |
SpikingJelly | 0.0.12 |
We will provide the trained models in the pretrained folder, which will include pretrained backbone networks and pretrained SFOD.
To evaluate or train SFOD you will need to download the datasets:
Dataset Name | Link |
---|---|
NCARS Dataset | Download N-CARS Car Classification Dataset | PROPHESEE |
GEN1 Dataset | Download Gen1 Automotive Detection Dataset | PROPHESEE |
python classification.py -devices auto -num_workers 8 -test -save_ckpt -model densenet-121_16 -loss_fun mse -encoding fre -early_stopping
python classification.py -devices auto -num_workers 8 -test -save_ckpt -model densenet-121_24 -loss_fun mse -encoding fre -early_stopping
python object_detection.py -devices auto -num_workers 4 -test -save_ckpt -backbone densenet-121_24 -pretrained_backbone ./pretrained/DenseNet121-24.ckpt -b 16 -fusion -fusion_layers 3 -mode res
When you perform evaluation, the corresponding pretrained model data needs to be replaced in the appropriate root folder.
python classification.py -devices auto -num_workers 8 -test -no_train -model densenet-121_16 -loss_fun mse -encoding fre -pretrained DenseNet121-16.ckpt
python classification.py -devices auto -num_workers 8 -test -no_train -model densenet-121_24 -loss_fun mse -encoding fre -pretrained DenseNet121-24.ckpt
python object_detection.py -num_workers 4 -test -no_train -pretrained SFOD.ckpt -backbone densenet-121_24 -fusion -fusion_layers 3 -mode res
This code is based on object-detection-with-spiking-neural-networks . Thanks to the contributors of object-detection-with-spiking-neural-networks .
@InProceedings{Cordone_2022_IJCNN,
author = {Cordone, Loic and Miramond, Benoît and Thierion, Phillipe},
title = {Object Detection with Spiking Neural Networks on Automotive Event Data},
booktitle = {Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN)},
month = {July},
year = {2022},
pages = {}
}