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How to choose the best computer vision model for your use case

Copyright 2024 - All Rights Reserved.

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Trusted by leading Fortune 500 enterprises and AI-fueled companies

Trusted by leading startups and Fortune 500 enterprises

MIKIKO BAZELEY

Head of DevRel - Labelbox

The rapid development and release of computer vision models, both discriminative and generative, means an increasing amount of options and complexity for developers and ML teams, when it comes to choosing which AI models to use for building intelligent applications.

In this webinar, we’ll be shedding light into best practices that teams can adopt for picking the computer vision models best-suited for their unique use cases. These frameworks can be applied to both teams looking to use off-the-shelf AI models for rapid prototyping, as well as teams building task specific models that require high-volumes of labeling, iterative model training, and deploying image-based systems.
 
We’ll uncover how to:

  - Understand the most popular and emerging use cases of computer vision models (including foundation models such as SAM, GPT-4V, YOLOv8)

  - Select the right (and best) CV model using specific evaluation criteria

  - Scale image-based applications using CV models (including LVM’s) for production by improving data quality through labeling automation