YOLOv5
YOLOv5 ? is a family of object detection architectures and models pretrained on the COCO dataset.
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YOLO an acronym for 'You only look once', is an object detection algorithm that divides images into a grid system. Each cell in the grid is responsible for detecting objects within itself. YOLO is one of the most famous object detection algorithms due to its speed and accuracy.
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Documentation
Documentation & Developer Guides
Guide for integrating W&B with YOLOv5
W&B is directly integrated into YOLOv5, providing experiment metric tracking, model and dataset versioning, rich model prediction visualization, and more
Track and debug your YOLOv5 models
Automatic bounding box debugging, system metrics, model performance metrics in the cloud, and shareable and reproducible model training for YOLOv5, using W&B
YOLOv5 Object Detection on Windows (Step-By-Step Tutorial)
This tutorial guides you through installing and running YOLOv5 on Windows with PyTorch GPU support
Collect and Label Images to Train a YOLOv5 Object Detection Model in PyTorch
This tutorial will guide you on how to prepare datasets to train custom YOLOv5 model step by step.
How are your YOLOv5 models doing?
Why is bounding box debugging a necessity?
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