From 80f699ae218f08d7f7846a7991c014456a81ae96 Mon Sep 17 00:00:00 2001 From: Muhammad Rizwan Munawar Date: Tue, 30 Jul 2024 17:11:30 +0500 Subject: [PATCH] Add https://youtu.be/_gRqR-miFPE & https://youtu.be/CfbHwPG01cE to docs (#14817) --- docs/en/guides/steps-of-a-cv-project.md | 11 +++++++++++ docs/en/models/yolov10.md | 11 +++++++++++ 2 files changed, 22 insertions(+) diff --git a/docs/en/guides/steps-of-a-cv-project.md b/docs/en/guides/steps-of-a-cv-project.md index a734ecf29b..a1fbdb5e97 100644 --- a/docs/en/guides/steps-of-a-cv-project.md +++ b/docs/en/guides/steps-of-a-cv-project.md @@ -10,6 +10,17 @@ keywords: Computer Vision, AI, Object Detection, Image Classification, Instance Computer vision is a subfield of artificial intelligence (AI) that helps computers see and understand the world like humans do. It processes and analyzes images or videos to extract information, recognize patterns, and make decisions based on that data. +

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+ Watch: How to Do Computer Vision Projects | A Step-by-Step Guide +

+ Computer vision techniques like [object detection](../tasks/detect.md), [image classification](../tasks/classify.md), and [instance segmentation](../tasks/segment.md) can be applied across various industries, from [autonomous driving](https://www.ultralytics.com/solutions/ai-in-self-driving) to [medical imaging](https://www.ultralytics.com/solutions/ai-in-healthcare) to gain valuable insights.

diff --git a/docs/en/models/yolov10.md b/docs/en/models/yolov10.md index 4646eb5d45..98a164a42e 100644 --- a/docs/en/models/yolov10.md +++ b/docs/en/models/yolov10.md @@ -10,6 +10,17 @@ YOLOv10, built on the [Ultralytics](https://ultralytics.com) [Python package](ht ![YOLOv10 consistent dual assignment for NMS-free training](https://github.com/ultralytics/ultralytics/assets/26833433/f9b1bec0-928e-41ce-a205-e12db3c4929a) +

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+ Watch: How to Train YOLOv10 on SKU-110k Dataset using Ultralytics | Retail Dataset +

+ ## Overview Real-time object detection aims to accurately predict object categories and positions in images with low latency. The YOLO series has been at the forefront of this research due to its balance between performance and efficiency. However, reliance on NMS and architectural inefficiencies have hindered optimal performance. YOLOv10 addresses these issues by introducing consistent dual assignments for NMS-free training and a holistic efficiency-accuracy driven model design strategy.