# OpenVINO-YOLOV4
## Introduction
This is full implementation of [YOLOV4 series](https://github.com/AlexeyAB/darknet) in OpenVINO2021.3.
Based on https://github.com/mystic123/tensorflow-yolo-v3
**Supported model**
- YOLOv4
- YOLOv4-relu
- YOLOv4-tiny
- [YOLOv4-tiny-3l](https://github.com/TNTWEN/OpenVINO-YOLOV4/tree/v4-tiny-3l)
- [YOLOv4-csp](https://github.com/TNTWEN/OpenVINO-YOLOV4/tree/ScaledYOLOv4)
- [YOLOv4x-mish](https://github.com/TNTWEN/OpenVINO-YOLOV4/tree/ScaledYOLOv4)
**Supported device**
- Intel CPU
- Intel GPU
- HDDL VPU
- NCS2
- ... ...
**Supported model precision**
- FP32
- FP16
- [INT8 Quantization](https://github.com/TNTWEN/OpenVINO-YOLOV4#int8-quantization)
**Supported inference demo**
- Python demo:all models
- C++ demo:YOLOv4,YOLOv4-relu,YOLOv4-tiny,YOLOv4-tiny-3l
## Environment
- OpenVINO2021.3 :https://docs.openvinotoolkit.org/latest/index.html or OpenVINO2020.4
- If you want to use yolov4+GPU+FP16,please don't use OpenVINO 2021.1 and OpenVINO2021.2
- Win or Ubuntu
- Python 3.6.5
- Tensorflow 1.15.5 (1.12.0 for OpenVINO2020.4 )
- YOLOV4:https://github.com/AlexeyAB/darknet train your own model
- *Convert YOLOV3/2/1 model :https://docs.openvinotoolkit.org/latest/openvino_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_YOLO_From_Tensorflow.html
## How to use
★ This repository provides python inference demo for different OpenVINO version.[pythondemo](https://github.com/TNTWEN/OpenVINO-YOLOV4/tree/master/pythondemo)
★ Choose the right demo before you run object_detection_demo_yolov3_async.py
★ You could also use C++ inference demo provided by OpenVINO.
(OpenVINO2021.3 default C++ demo path:`C:\Program Files (x86)\Intel\openvino_2021.3.394\deployment_tools\open_model_zoo\demos\multi_channel_object_detection_demo_yolov3\cpp`)
### YOLOV4
download yolov4.weights .
```
#windows default OpenVINO path
python convert_weights_pb.py --class_names cfg/coco.names --weights_file yolov4.weights --data_format NHWC
"C:\Program Files (x86)\Intel\openvino_2021\bin\setupvars.bat"
python "C:\Program Files (x86)\Intel\openvino_2021.3.394\deployment_tools\model_optimizer\mo.py" --input_model frozen_darknet_yolov4_model.pb --transformations_config yolov4.json --batch 1 --reverse_input_channels
python object_detection_demo_yolov3_async.py -i cam -m frozen_darknet_yolov4_model.xml -d CPU
```
![OpenVINOyolov4](assets/yolov4-416.png)
Compared with darknet:
![darknetyolov4](assets/darknet-v4-416.jpg)
### YOLOV4-relu
prepare yolov4.weights .
```
#windows default OpenVINO path
cd yolov4-relu
python convert_weights_pb.py --class_names cfg/coco.names --weights_file yolov4.weights --data_format NHWC
"C:\Program Files (x86)\Intel\openvino_2021\bin\setupvars.bat"
python "C:\Program Files (x86)\Intel\openvino_2021.3.394\deployment_tools\model_optimizer\mo.py" --input_model frozen_darknet_yolov4_model.pb --transformations_config yolov4.json --batch 1 --reverse_input_channels
python object_detection_demo_yolov3_async.py -i cam -m frozen_darknet_yolov4_model.xml -d CPU
```
### YOLOV4-tiny
download yolov4-tiny.weights .
```
#windows default OpenVINO path
python convert_weights_pb.py --class_names cfg/coco.names --weights_file yolov4-tiny.weights --data_format NHWC --tiny
"C:\Program Files (x86)\Intel\openvino_2021\bin\setupvars.bat"
python "C:\Program Files (x86)\Intel\openvino_2021.3.394\deployment_tools\model_optimizer\mo.py" --input_model frozen_darknet_yolov4_model.pb --transformations_config yolo_v4_tiny.json --batch 1 --reverse_input_channels
python object_detection_demo_yolov3_async.py -i cam -m frozen_darknet_yolov4_model.xml -d CPU
```
![OpenVINOyolov4tiny](assets/yolov4tiny416.png)
Compared with darknet:
![darknetyolov4tiny](assets/darknet-v4tiny-416.jpg)
## INT8 Quantization
Thanks for [Jacky](https://github.com/jayer95)'s excellent work!
Ref:https://docs.openvinotoolkit.org/latest/pot_README.html
Environment:
- OpenVINO2021.3
- Ubuntu 18.04/20.04 ★
- Intel CPU/GPU
**Step 1:Dataset Conversion**
we should convert YOLO dataset to OpenVINO supported formats first.
|--annotations
|-- output.json #output of convert.py , COCO-JSON format
|--images
|-- *.jpg #put all the images here
|--labels
|--*.txt #put all the YOLO format .txt labels here
|--classes.txt
we use coco128 for example:
```
cd INT8
python3 convert.py --root_dir coco128 --save_path output.json
```
**Step 2: Install Accuracy-checker and POT**
```
sudo apt-get install python3 python3-dev python3-setuptools python3-pip
cd /opt/intel/openvino_2021.3.394/deployment_tools/open_model_zoo/tools/accuracy_checker
sudo python3 setup.py install
cd /opt/intel/openvino_2021.3.394/deployment_tools/tools/post_training_optimization_toolkit
sudo python3 setup.py install
```
**Step 3: INT8 Quantization using POT**
Prepare your yolo IR model(FP32/FP16) first.
```
source '/opt/intel/openvino_2021.3.394/bin/setupvars.sh'
pot -c yolov4_416x416_qtz.json --output-dir backup -e
```
Parameters you need to set in yolov4_416x416_qtz.json:
- Line 4,5 :Set FP32/FP16 YOLO IR model 's path
```
"model":"models/yolov4/FP16/frozen_darknet_yolov4_model.xml",
"weights":"models/yolov4/FP16/frozen_darknet_yolov4_model.bin"
```
- Line 29,30 :Set image width and height
```
"dst_width": 416,
"dst_height": 416
```
- Line 38: Annotation_file(COCO JSON file)
```
"annotation_file": "./coco128/annotations/output.json"
```
- Line 40: Path of images
```
"data_source": "./coco128/images",
```
- There are many other quantization strategies to choose from, and the relevant parameters are annotated in yolov4_416x416_qtz.json.Select the strategy you want to replace the default strategy and try by yourself!
**Step 4: Test IR model's map using Accuracy-checker**
```
#source '/opt/intel/openvino_2021.3.394/bin/setupvars.sh'
accuracy_check -c yolov4_416x416_coco.yml -td CPU #-td GPU will be faster
```
Parameters you need to set in yolov4_416x416_qtz.json:
- Line 5,6 : Set IR model 's path
```
model: models/yolov4/FP16/frozen_darknet_yolov4_model.xml
weights: models/yolov4/FP16/frozen_darknet_yolov4_model.bin
```
- Line 12: number of classes
```
classes: 80
```
- Line 25: Image size
```
size: 416
```
- Line 38:Annotation_file(COCO JSON file)
```
annotation_file: ./coco128/annotations/output.json
```
- Line 39: Path of images
```
data_source: ./coco128/images
```
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算法部署-使用Openvino部署YOLOv4目标检测算法-支持INT8量化-优质算法部署项目实战.zip (299个子文件)
yolov4-relu.cfg 12KB
yolov4-relu.cfg 12KB
yolov4.cfg 12KB
yolov4.cfg 12KB
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