YOLOv5s

Using the RK3588 Chip NPU: Running and Interpreting the Official rknn_yolov5_android_apk_demo

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Using the RK3588 Chip NPU: Running and Interpreting the Official rknn_yolov5_android_apk_demo

1. Objective of This Article This article will accomplish two tasks: Run the official dynamic target recognition example using the camera on the RK3588 development board. Interpret the source code to enhance understanding of the rknn development. 2. Development Environment Description Host System: Windows 11 Target Device: Android development board equipped with RK3588 chip Core … Read more

Using the RK3588 Chip NPU: Running YOLOv5 Object Detection Model on Windows 11 with RKNN Docker

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Using the RK3588 Chip NPU: Running YOLOv5 Object Detection Model on Windows 11 with RKNN Docker

Objective of This Article This article will detail how to configure the RKNN Docker environment on an Android development board equipped with the RK3588 chip in a Windows 11 system environment, and how to run the YOLOv5 object detection model accelerated by NPU on the development board using the adb tool. Development Environment Description Host … Read more

Practical Guide to Achieving 10x Inference Speed with YOLOv5: Model Deployment Using TensorRT on Jetson NX

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Practical Guide to Achieving 10x Inference Speed with YOLOv5: Model Deployment Using TensorRT on Jetson NX

Follow our public account to discover the beauty of CV technology This article is adapted from the Frontier of Aerial Robotics, authored by Liang Jiachen, an engineer at Westlake University. With the continuous improvement of computing power and the growth of data, deep learning algorithms have made significant advancements. These algorithms are increasingly applied across … Read more

Deploying YOLOV5 on RK3399Pro Development Board

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Deploying YOLOV5 on RK3399Pro Development Board

1. Hardware Devices (1) RK3399Pro Development Board: This is a development board launched by Rockchip, equipped with NPU (Neural-network Processing Units), supporting 8-bit and 16-bit operations, with a computing performance of up to 3.0 TOPs. Compared to similar NPU chips, its performance is leading by as much as 150%, and it is compatible with various … Read more

Animal Target Detection Based on YOLOv5 and Raspberry Pi 4B

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Animal Target Detection Based on YOLOv5 and Raspberry Pi 4B

Object detection is of great significance in the field of computer vision. YOLOv5 (You Only Look One-level) is a representative method among object detection algorithms, renowned for its efficiency and accuracy, and has shown outstanding performance in various object detection tasks. This article will detail how to train the YOLOv5 model on a more powerful … Read more

Deploying Yolov5 on Raspberry Pi for Object Detection

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Deploying Yolov5 on Raspberry Pi for Object Detection

Click the card below to follow the “New Machine Vision” public account Important content delivered at the first time 1. Task Description Realize the identification of workpieces through machine vision, using Raspberry Pi as the host, issuing different instructions to the lower machine based on different identification effects, controlling the entire machine operation. The process … Read more

Deploying and Evaluating the RK3588 YOLOv5s Model

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Deploying and Evaluating the RK3588 YOLOv5s Model

MEGAWAY TECHNOLOGY RK3588 YOLOv5s Model Deployment and Evaluation 01/ Model Overview Model Name: YOLOv5s Model Type: Classification Model Official Repository: GitHub – ultralytics/yolov5: YOLOv5 in PyTorch> ONNX > CoreML > TFLite V7.0 Model Parameters (PARAMS): 7225885 Model Computation (FLOPS): 16.4 GFLOPs Deployment Device: RK3588 Deployment Environment: Ubuntu20.04/rknn_toolkit2 V1.6.0/OpenCV4.5.1 02/ Model Analysis YOLOV5 model outputs three … Read more