# yolo_quantization

**Repository Path**: lbing9002/yolo_quantization

## Basic Information

- **Project Name**: yolo_quantization
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-12-29
- **Last Updated**: 2023-12-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# yolo_quantization
The code is to quantization **float32 network** of darknet to **uint8 network** based of paper:

>**Quantization and Training of Neural Networks for Efficient**

< https://arxiv.org/abs/1712.05877 >

[The Commond to Run My Project]
=========
Train: 
>**set GPU=1 in Makefile**

	make -j8

	./darknet detector train cfg/voc_nok.data cfg/yolov3-tiny-mask_quant.cfg [pretrain weights file I gave to you(default in cfg folder)]

Test:
>**set GPU=0 in Makefile**
	
	make -j8
	
	./darknet detector test cfg/voc_nok.data cfg/yolov3-tiny_quant.cfg [weights file] [image path]

[Pretrain Cfg file and Weights file]
=========
	https://pan.baidu.com/s/16_ULXdNPmIhoEmu7jXmkmQ 
	password: qy8a 
	
[Performance]
=========
 | quantization | inference time (intel chip 64bit) | recall | precision | f1 score |
 | :------: | :------: | :------: | :------: | :------: |
 | darknet | 0.83s | 74.43 | 89.45 | 81.25| 
 | quantization mine | 0.34s | 90.08 | 91.83 | 90.94 |