The content is also available as text: https://github.com/adensur/blog/blob/main/computer_vision_zero_to_hero/10_yolo_object_detection/Readme.md
This video talks about YOLO algorithm for object detection.
It talks about model architecture, loss, non-maximum suppression and anchor boxes.
Docs for the model: https://docs.ultralytics.com/yolov5/
PapersWithCode benchmark: https://paperswithcode.com/sota/object-detection-on-coco?tag_filter=15

This video is part of my “Modern Object Detection: from YOLO to transformers” series: https://www.youtube.com/playlist?list=PL1HdfW5-F8AQlPZCJBq2gNjERTDEAl8v3

00:00 – Intro
06:21 – Comparison to Image Recognition Models
08:21 – YOLO Algorithm
10:03 – Confidence Prediction
11:48 – Comparison to other Object Detectors
13:32 – YOLO Algorithm
16:53 – Training Process
19:18 – Inference Process & NMS
22:09 – Anchor Boxes
25:06 – Loss
28:31 – Results

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