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