Yolov5
A state-of-the-art PyTorch-based computer vision model for object detection, segmentation, and classification.
What is Yolov5?
Ultralytics YOLOv5 is a state-of-the-art computer vision model built on PyTorch, designed for object detection, instance segmentation, and image classification. It solves the problem of performing real-time, accurate visual analysis by providing a suite of pre-trained models that are easy to train, validate, and deploy across various hardware and software platforms.
Computer vision researchers, developers, and engineers working on real-time object detection, segmentation, or classification tasks, particularly those who value ease of use, speed, and a wide range of deployment options.
Developers choose YOLOv5 for its balance of speed and accuracy, its extensive model zoo ranging from nano to extra-large sizes, and its straightforward workflow for training on custom data and exporting to multiple formats like ONNX, TensorRT, and TFLite for production deployment.
Overview
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Use Cases
Best For
- Real-time object detection in video streams from webcams, RTSP feeds, or video files.
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