OpenVINO-YOLOV4
- 0 Collaborators
This is implementation of YOLOV4,YOLOV4-relu,YOLOV4-tiny ,YOLOV4-tiny-3l ,Scaled-YOLOv4 in OpenVINO2021.3 ...learn more
Project status: Published/In Market
Intel Technologies
Intel FPGA,
Movidius NCS,
OpenVINO
Overview / Usage
Suppported models:
- YOLOv4 (python C++ demo)
- YOLOv4-relu (python C++ demo)
- YOLOv4-tiny (python C++ demo)
- YOLOv4-tiny-3l (python C++ demo)
- YOLOv4-CSP (only python demo now)
- YOLOv4x-mish (only python demo now)
Supported model precision:
- FP32
- FP16
- INT8 Quantization
Optimization strategy:
- Prune YOLOv4 series model and quickly deploy pruned model in openvino:https://github.com/TNTWEN/Pruned-OpenVINO-YOLO
- INT8 Quantization using OpenVINO POT. Pruned-YOLOv4 series model+ INT8 Quantization will be very friendly to embedded devices
Methodology / Approach
Based on https://github.com/mystic123/tensorflow-yolo-v3
darknet->tensorflow1.x->OpenVINO
Technologies Used
OpenVINO2020.4 OpenVINO2021.3
OpenVINO POT and Accuracy-checker
ALL intel inference device