🙌 OpenHands: Code Less, Make More

Convolutional Neural Networks

[CVPR 2024 Highlight] Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching

Swap faces in images and videos. Create face embeddings. Enhance face image quality. Deploy as a web api.

PLPR utilizes YOLOv5 and custom models for high-accuracy Persian license plate recognition, featuring real-time processing and an intuitive interface in an open-source framework.

UC3M License Plate detection and recognition dataset

Persian raw text - حدود ۸۰ گیگابایت متن خام فارسی

Label Studio is a multi-type data labeling and annotation tool with standardized output format

LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source …

Python tool to easily label objects in images with bounding boxes for YOLO training. It is powered by Segment Anything Model (SAM), by Meta AI, that allows to get precise bounding boxes around obje…

Persian LicensePlate Recognition System using YOLO11 and OpenCV

Face detection with MNN for Raspberry Pi 4

ncnn benchmark on various single board computers

An example of using the clash royale API in python

Begzar, is a free software for internet freedom.

enable vulkan on mali610

A License Plate Image Reconstruction Project in Tensorflow2

YoloFastestV2 for a bare Raspberry Pi 4

⚡ Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+

Understand yolov8 structure,custom data traininig

Aggregation of packages and steps for using YOLOv5 Object Detection on Orange Pi 5 Plus NPU

Pytorch Implementation For LPRNet, A High Performance And Lightweight License Plate Recognition Framework.

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

YOLOv5 for RK3588

A simple demo of yolov5s running on rk3588/3588s using c++ (about 142 frames). / 一个使用c++在rk3588/3588s上运行的yolov5s简单demo(142帧/s)。

export any your YOLOv7 model to TensorFlow, TensorFlowJs, ONNX, OpenVINO, RKNN,...

simple yolov5 rtspserver for rk3588

Inference YOLOv8 detection on ONNX, RKNN, Horizon and TensorRT