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

# 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 ```  Compared with darknet:  ### 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 ```  Compared with darknet:  ## 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 ```