This repository offers a PyTorch implementation of YOLOv5 for object detection, enabling efficient training and deployment across diverse platforms.
YOLOv5 is a state-of-the-art object detection model, built with PyTorch, designed for real-time applications. It facilitates training custom models and exporting them to formats like ONNX, CoreML, and TFLite, supporting deployment on various devices including mobile. Getting started typically involves a `git clone` and `pip install -r requirements.txt`, followed by running `train.py` or `detect.py` scripts.
This repository offers a PyTorch implementation of YOLOv5 for object detection, enabling efficient training and deployment across diverse platforms.
Machine learning practitioners, researchers, and developers aiming to build and deploy fast, accurate object detection solutions.