This project consists on implementing a YOLOv3 object detector on a PYNQ-Z2 PLD, making use of the Zynq-7020 to accelerate the inference and provide accurate results. In the end, the user should be able to obtain the detection (bounding boxes, class and probability) of an input image.

2023年10月23日 · 本文详细介绍了如何在PYNQZ2开发板上使用2.5版本的镜像文件，包括格式化SD卡、安装PYNQ例程、设置网络连接并运行Objectdetectiondemo的过程。

2020年6月13日 · pynq-z2复现yolo-v2工程，已经更换了新的权重。 使用 PYNQ Z 2 实现目标检测 YOLO （从零到一的项目构建） 为了梦想而努力奋斗

2024年3月20日 · 本文章记述了从YOLOv5 源代码 使用Xilinx Vitis AI进行量化并部署到DPU上的全流程。 在开Pynq环境下运行测试通过。 主机: Ubuntu 22.04 + Vivado 2022.2 + Vitis AI 2.5.0 （使用Docker安装）+ CUDA 11.3. 开发板：Xilinx Kria KV260 + Pynq 3.0 + DPU Pynq 2.5.1. 版本是很重要的！ 本代码使用Pynq作为程序界面，因此DPU-PYNQ的版本支持性决定了大多数的版本需求。 Ubuntu和Vivado的版本一般来讲没有关系，但要注意Vivado是否支持Kria的芯片型号。 截 …

A Demo for accelerating YOLOv2 in Xilinx's FPGA PYNQ-z2, Zedboard and ZCU102 I have graduated from Jiangnan University, China in July 1, 2019. Related papers are available now. Master thesis "Research of Scalability on FPGA-based Neural Network Accelerator"

This project consists on implementing a YOLOv3 object detector on a PYNQ-Z2 PLD, making use of the Zynq-7020 to accelerate the inference and provide accurate results. In the end, the user should be able to obtain the detection (bounding boxes, class and probability) of an input image.

YOLO for PYNQ-Z1 and Intel/Movidius Neural Compute Stick (NCS) This project is derived from yoloNCS and is intended to be used on the PYNQ-Z1 board. Using this repo on your PYNQ-Z1

The PYNQ-Z2 board is a Xilinx development board that takes part on the PYNQ family (Python Productivity for Zynq). This board is the one recommended by Xilinx to get started on the FPGA development because of its easy interaction with the Zynq-7020 (FPGA) using python programming language.

A full guide to implement a YOLOv3 object detector on a PYNQ-Z2 FPGA.

2020年5月19日 · PYNQ provides a Python interface that allows you to control overlays in PL through Python running in PS. FPGA design is a professional task that requires hardware engineering knowledge and professional knowledge. PYNQ's Overlay layer is created by hardware designers and is included in this PYNQ Python API.

pynq_yolov2 介绍 基于pynq的yolov2目标检测 软硬件配置 PYNQ-Z2开发板、USB数据线、网线、SD卡、读卡器 PYNQ Image: PYNQ v2.6 vivado/HLS：2020.1 项目实现 搭建HLS工程，生成Yolo_v2的IP（[yolov2源项目链接](源项目工程 (github.com))) 在Vivado中使用生成好的IP导出bit文 …

The original project had the goal to implement a real-time object detector on the PYNQ-Z2, making use of the Zynq-7020 FPGA to accelerate the inference time. I'm proud to say it was achieved, although the frame rate isn't impressive.

By leveraging the power of Field-Programmable Gate Arrays (FPGAs) and utilising both the fpgaConvNet and the Xilinx PYNQ frameworks, this solution enables high-performance and efficient execution of object detection tasks. A high-level overview of the proposed solution for YOLOv5 acceleration with fpgaConvNet and PYNQ.

这篇博客详细介绍了如何在PYNQ-Z2开发板上复现Yolo_v2神经网络。 首先，利用Vivado HLS 2018.3生成Yolo_v2 IP核，然后在Vivado 2018.3中进行Block Design，最终在PYNQ-Z2上运行实现目标检测。

To address this problem, a YOLO model accelerator architecture is proposed based on PYNQ. Based on the FPGA hardware platform, the hardware accelerator is designed by making full use of pipeline, loop unrolling, data reordering and other methods to accelerate the computationally intensive units in the YOLOv2 model such as the convolution and ...

This repo contains evaluation and deploy scripts for official LPYOLO: Low Precision YOLO for Face Detection on FPGA paper. Models are trained by Brevitas which is a PyTorch research library for quantization-aware training (QAT) and exported to ONNX.

It's time to add all the things together and execute the YOLOv3 Neural Network in order to perform inference on a desired image. Previously, we've created a package containing the DPU elf file and other necessary files. We will now copy that package to the board's Operating System using the Xterm interface.

2024年7月4日 · 本文介绍了如何在fpga平台上使用pynq进行yolo目标检测的加速。 首先讲解了开发板内存卡的装配和设置以连接到Pynq，然后详细阐述了如何配置网络使得开发板与笔记本电脑共享网络。

Deployment on PYNQ-Z2. On this chapter we will address all the preparation steps to setup the board and how to execute YOLO on the PYNQ-Z2.

This tool allows to quantize and optimize Deep Learning models and also compile, in other words, transform the quantized model in a binary file so it later can interact with the DPU. Without this reduction in size, the original YOLO model wouldn't be compatible with the PYNQ-Z2 because of it's restricted memory.