In this video, we dive into the complexities 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 plan to develop an app for counting public transport commuters or for analyzing medical images, we will guide you through the critical factors that should be considered in your model choice. We even explore specific models such as 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 Thinking about model selection
01:36 Different project contexts (counting people versus 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 data sets
08:27 Library packaging
09:46 Model integration and the role of SDKs
10:52 Importance of active project support
11:27 Understanding project licensing
12:31 Conclusion

Sources:

Roboflow: https://roboflow.com
Roboflow universe: https://universe.roboflow.com
Roboflow Notebooks repository: https://github.com/roboflow/notebooks

YOLOv8: Training for object detection on a custom dataset: https://youtu.be/wuZtUMEiKWY

Stay up to date with the projects I'm working on at https://github.com/roboflow and https://github.com/SkalskiP!

Don't forget to like, comment and subscribe for more content on AI, computer vision and the latest technological breakthroughs!

#ComputerVision #ObjectDetection #InstanceSegmentation #DeepLearning #YOLO #Detectron2 #Dataset #ModelSelection #AI #YOLOv8

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