YOLOv5 Tutorial | Architecture, Assigning Targets & Loss Function Explained

In this video, we deep dive into YOLOv5 — covering its architecture, specifically changes from yolov4. We get into how in this version of yolo, we assign predictions to targets and also go through the pytorch implementation of that.From there we move to YOLOv5 loss function, then cover AutoAnchor mechanism, and lastly results that yolov5 object detection model achieves. This tutorial video is divided into following parts: - YOLOv5 Architecture (Backbone, Neck, Head) - Most of the layers are similar to yolov4. The video goes over the specific changes to some of those blocks and layers as we transition from yolov4 to yolov5 - Anchor Matching: How predictions are assigned to targets - Unlike earlier versions now multiple grid cell are responsible predictors for a target and same anchor grid cell pair can be assigned to multiple ground truth boxes. - YOLOv5 Loss Function (Box loss, objectness, classification) - Very similar to yolov4 loss with some minor changes like objectness loss weights and using complete iou loss. - AutoAnchor: What it is and how it improves detection - YOLOv5 vs YOLOv4 performance and results Timstamps 00:00 Intro 01:14 YOLOv5 Architecture 07:31 Matching Targets to Predictions 16:25 Target Assignment PyTorch Implementation 25:33 Loss 27:22 Yolo v5 loss implementation in PyTorch 31:38 Augmentations 33:34 Autoanchor utility of YOLOv5 35:33 YOLOv5 Model Variants 39:57 yolo v5 object detection results