# ems-yolo

**Repository Path**: xing-yukai/ems-yolo

## Basic Information

- **Project Name**: ems-yolo
- **Description**: xixi
- **Primary Language**: Python
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2024-07-02
- **Last Updated**: 2024-07-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: Deep-learning

## README

<div align="center">



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## <div align="center">Deep Directly-Trained Spiking Neural Networks for Object Detection [(ICCV2023)](https://openaccess.thecvf.com/content/ICCV2023/html/Su_Deep_Directly-Trained_Spiking_Neural_Networks_for_Object_Detection_ICCV_2023_paper.html)</div>
</div>

### Requirements

The code has been tested with pytorch=1.10.1,py=3.8, cuda=11.3, cudnn=8.2.0_0 . The conda environment can be copied directly via <b>environment.yml</b>. Some additional dependencies can be found in the  <b>environment.txt</b>.


<details open>
<summary>Install</summary>

```bash
$ git clone https://github.com/BICLab/EMS-YOLO.git
$ pip install -r requirements.txt
```

</details>

### Pretrained Checkpoints

We provide the best and the last trained model based on EMS-Res34 on the COCO dataset.

`detect.py` runs inference on a variety of sources, downloading models automatically from
the [COCO_EMS-ResNet34](https://drive.google.com/drive/folders/1mry8sdED6ncqxajmQROKBECpcrmXStpB?usp=sharing) .

The relevant parameter files are in the `runs/train`.


### Training & Addition
<details open>
<summary>Train</summary>

The relevant code for the Gen1 dataset is at `/g1-resnet`. It needs to be replaced or added to the appropriate root folder.

For gen1 dataset:

```python
python path/to/train_g1.py --weights ***.pt --img 640
```
For coco dataset:
```python
python train.py
```
</details>


Calculating the spiking rate:

Dependencies can be downloaded from [Visualizer](https://github.com/luo3300612/Visualizer).
```python
python calculate_fr.py
```

### Contact Information


```shell
@inproceedings{su2023deep,
  title={Deep Directly-Trained Spiking Neural Networks for Object Detection},
  author={Su, Qiaoyi and Chou, Yuhong and Hu, Yifan and Li, Jianing and Mei, Shijie and Zhang, Ziyang and Li, Guoqi},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={6555--6565},
  year={2023}
}
```

<p>
YOLOv3  is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
 open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. 
 
 <b>Our code is also implemented in this framework, so please remember to cite their work.</b>
</p>