Yolo
Showing 16 of 16 projects
A cutting-edge framework for training and deploying state-of-the-art YOLO models for object detection, segmentation, classification, and pose estimation.
A state-of-the-art PyTorch-based computer vision model for object detection, segmentation, and classification.
An open source neural network framework in C and CUDA, known for YOLO real-time object detection models.
A PyTorch implementation of YOLOv3 for real-time object detection, supporting export to ONNX, CoreML, and TFLite.
A TensorFlow implementation of YOLO for real-time object detection, supporting weight conversion, training, and mobile deployment.
Self-hosted, local only NVR and AI Computer Vision software. With features such as object detection, motion detection, face recognition and more, it gives you the power to keep an eye on your home, office or any other place you want to monitor.
A ROS package for real-time object detection in camera images using YOLO (V3) on GPU and CPU.
A ROS package for real-time object detection in camera images using YOLO (V3) on GPU and CPU.
An open-source computer vision tool that detects, tracks, and counts moving objects from cameras and videos.
TensorFlow implementation of YOLO for real-time object detection using pretrained YOLO_small, YOLO_tiny, and YOLO_face models.
High-level TensorFlow network definitions with pre-trained weights for easy integration into existing ML workflows.
Real-time object detection on Android using YOLO with TensorFlow, detecting 20 object classes from the Pascal VOC dataset.
A benchmark dataset and toolkit for RF-based drone detection and identification using raw IQ data and deep learning models.
A Docker-based image annotation tool for bounding box labeling with auto-labeling support, designed for deep learning training.
A YOLO-based object detection system specifically trained to identify DJI drones in images and video.
A curated archive of pre-trained computer vision models for object detection, face recognition, fire detection, and more.
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