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https://github.com/ZLkanyo009/Yolo-compression-and-deployment-in-FPGA
基于FPGA量化的人脸口罩检测
https://github.com/ZLkanyo009/Yolo-compression-and-deployment-in-FPGA
fpga object-detection pytorch quantization risc-v yolov3
Last synced: 12 days ago
JSON representation
基于FPGA量化的人脸口罩检测
- Host: GitHub
- URL: https://github.com/ZLkanyo009/Yolo-compression-and-deployment-in-FPGA
- Owner: ZLkanyo009
- Created: 2021-06-17T09:16:05.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2021-08-24T06:09:47.000Z (almost 3 years ago)
- Last Synced: 2024-02-29T09:32:44.840Z (4 months ago)
- Topics: fpga, object-detection, pytorch, quantization, risc-v, yolov3
- Language: C
- Homepage:
- Size: 290 MB
- Stars: 16
- Watchers: 1
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Lists
- awesome-yolo-object-detection - ZLkanyo009/Yolo-compression-and-deployment-in-FPGA - compression-and-deployment-in-FPGA?style=social"/> : 基于FPGA量化的人脸口罩检测。 (Lighter and Deployment Frameworks)
- awesome-cuda-tensorrt-fpga - ZLkanyo009/Yolo-compression-and-deployment-in-FPGA - compression-and-deployment-in-FPGA?style=social"/> : 基于FPGA量化的人脸口罩检测。 (Applications)
README
# FPGA Yolov2 Mask detection
基于FPGA量化的人脸口罩检测
主框架使用：https://github.com/yjh0410/yolov2-yolov3_PyTorch
添加bn融合以及FPGA量化部分,并设计实际使用的网络结构slim_yolov2
数据集使用：https://github.com/AIZOOTech/FaceMaskDetection 内的人脸口罩数据集，转成VOC格式后使用
# Installation Dependencies:
- python3
- pytorch
- pycocotools
- numpy
- opencv
# Dataset
将数据集软连接到该文件夹的dataset下
example:
```Shell
ln -s /data/coco dataset/coco
ln -s /data/VOC dataset/VOC
```
# Visualization
# Train
在此只展示训练口罩检测时用的代码，其余command可以从https://github.com/yjh0410/yolov2-yolov3_PyTorch 中自行查找
## VOC
泛用用例：
```Shell
python train.py -d dataset --cuda -v [select a model] -hr -ms
```
训练FP32模型：
```Shell
python train.py -d mask --cuda -v slim_yolo_v2
```
BN融合：
```Shell
python conv+bn2conv.py -d mask --cuda -v slim_yolo_v2 -r your_weight_path
```
BN融合后微调精度：
```Shell
python retune_bias_quantize.py -d mask --cuda -v slim_yolo_v2_q_bf -r your_weight_path
```
FPGA模拟量化：
```Shell
python retune_bias_quantize.py -d mask --cuda -v slim_yolo_v2_q_bf -q -r your_weight_path
```
寻找FPGA定点计算时防止溢出而加上的每层的scale_retune：
```Shell
python retune_bias_quantize_findbest.py -d mask --cuda -v slim_yolo_v2_q_bf -q -r your_weight_path
```
You can run ```python train.py -h``` to check all optional argument.
.
# test
## VOC
```Shell
python test.py -v slim_yolo_v2_q_bf -d mask --cuda --trained_model your_weight_path
```
# Detection
```Shell
python demo.py -v slim_yolo_v2_q_bf --mode camera --trained_model your_weight_path
```