How to Choose the Best Computer Vision Model for Your Project
In this video, we will dive into the complexity of choosing the right computer vision model for your unique project.
From the importance of high-quality datasets to hardware considerations, interoperability, benchmarking, and licensing issues, this video covers it all. Whether you're planning to develop an app for counting commuters in public transport or analyzing medical images, we guide you on the critical factors that should inform your model selection. We even explore specific models like YOLOv5, YOLO-NAS, and Detectron2 in context. Don't forget to like, subscribe, and stay tuned for more computer vision content!
Chapters:
00:00 Introduction
00:40 Overthinking Model Selection
01:36 Different Project Contexts (Counting People vs Analyzing Medical Images)
03:15 Hardware Considerations
04:04 mAP vs Latency
05:33 Benchmarking and the Importance of Preliminary Testing
06:00 Understanding mAP Values in the Context of Custom Datasets
08:27 Library Packaging
09:46 Model Integration and the Role of SDKs
10:52 Importance of Active Project Support
11:27 Understanding Project Licenses
12:31 Conclusion
Resources:
Object Detection Model Leaderboard: https://roboflow.com/models/leaderboard
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📚 Roboflow Notebooks Repository: https://github.com/roboflow/notebooks
🎬 YOLOv8: How to Train for Object Detection on a Custom Dataset: • YOLOv8: How to Train for Object Detec...
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#ComputerVision #ObjectDetection #InstanceSegmentation #DeepLearning #YOLO #Detectron2 #Dataset #ModelSelection #AI #YOLOv8
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