How Many Images Do I Need to Train a Computer Vision Model? | Best Practices in 2023

Описание: In this comprehensive guide, the co-founder and CEO of Abud AI, delves into creating a high-quality computer vision dataset. The tutorial focuses on best practices for dataset creation, including bounding box precision, class accuracy, and the number of images for tasks like object detection, image classification, and segmentation.

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#ComputerVision, #AI, #MachineLearning, #DataScience, #ObjectDetection, #ImageClassification, #TechTutorial

Chapters:
00:05: Introduction by Rama, CEO of Abud AI
00:08: Overview of Creating Computer Vision Datasets
00:39: Principles for Dataset Creation in AI Subfields
01:06: Determining the Number of Images Needed
01:39: Exploring Examples and Use Cases
07:03: Importance of Object Classes in Datasets
09:32: Blood Cell Detection Dataset Example
11:07: Counting Oranges: A Case Study
12:20: Supermarket Shelf Product Detection
13:48: Self-Driving Cars: A Comprehensive Example
15:14: Importance of Quality Labeling in Datasets
18:02: Using Auto-Labeling to Enhance Dataset Creation
20:29: Demonstration: Effective Model with Limited Images
23:06: Final Remarks and Best Practices Summary

Links and Resources:
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🤓 [Read our blog](https://blog.abud.ai)
📚 [Explore Abud AI official documentation](https://docs.abud.ai/deployment#use-y...)
⚡️ [Clone the Easy YOLOv7 repo](https://github.com/abud-ai/easy-yolov7)

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