Data challenges for Computer Vision cases | Daria Lashkevich
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📩 In this presentation, Daria Lashkevich, Computer Vision Engineer at Accenture, discusses the challenges in building machine learning models for computer vision, especially in specialized industries. From creating custom labeling tools to overcoming data quality issues, Daria highlights real-world examples, including car manufacturing quality checks and wildlife species detection, and how to tackle common pitfalls in computer vision projects.
🔍 In This Video, You’ll Learn About:
✅ The challenges of manual data labeling for computer vision models
✅ How to use few-shot learning for faster and more efficient labeling
✅ The process of training models for industrial applications like car assembly
✅ How to use synthetic data generation for difficult-to-collect data
✅ The importance of data quality and argumentation in model performance
✅ Real-world examples of computer vision in automotive and wildlife applications
00:00 🚀 Build Stuff Conference – Get Your Tickets!
00:21 Introduction to Data Challenges in Computer Vision
02:33 The Role of Data Labeling in Computer Vision Models
05:14 Overcoming Labeling Challenges with Few-Shot Learning
08:30 Custom Labeling Tools for Specific Applications
12:19 Case Study: Detecting Car Assembly Defects
16:40 Generating Synthetic Data for Hard-to-Collect Use Cases
19:02 Using Camera Angles and Lighting to Improve Data Quality
22:14 Real-Time Computer Vision for Car Manufacturing
25:28 Case Study: Wildlife Species Detection in the Jungle
30:05 Addressing Data Imbalances in Computer Vision Projects
34:10 The Future of Computer Vision and Data Collection Techniques
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