Yolo
Showing 15 of 15 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.
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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