Building CV-powered apps | Vercel AI Accelerator (Roboflow)

TL;DR

Joseph Nelson, CEO of Roboflow, shares insights on building and deploying computer vision products using existing models, fine-tuning models, and training custom models.

Transcript

hello everybody thank you all so much for joining welcome to the second fireside chat of the versel AI accelerator program uh today we're going to have Joseph Nelson of roboflow talk to us about building and deploying computer vision products I'm especially excited uh for this one because it's actually quite applicable to a few of the companies we ... Read More

Key Insights

• 🔍 Computer vision is being used in various industries and applications, from remote physical therapy tools to calorie counting apps.
• 🚀 Roboflow is a company that provides tools for developers to build computer vision products, with over 250,000 developers using their tools.
• 💡 Computer vision enables apps to turn images and videos into real actions, making the world programmable and delivering more engaging experiences.
• 📸 Examples of computer vision applications include flame-throwing weed-killing robots, exercise machines for cats, and geo-referencing projects using drones.
• 🎯 There are three scenarios when using computer vision: using an existing model as is, using an existing model partially and improving it, or building a custom model.
• 🔍 In the scenario where an existing model works as is, developers can make use of foundation models like OpenAI's CLIP for semantic search and retrieval.
• 📚 In the scenario where an existing model partially works, developers can use distillation techniques to extract insights from large models and train smaller task-specific models.
• 🔧 In the scenario where there is no existing model, developers can train their own custom models using their own data and deploy them for specific use cases.

Questions & Answers

Q: What are the advantages of using existing foundation models for computer vision tasks?

Using existing foundation models like CLIP or Flamingo can save time and effort in developing a computer vision solution. These models have been trained on large datasets and can provide accurate results for common use cases without the need for extensive training with custom data.

Q: When should developers consider fine-tuning existing models instead of training custom models?

Fine-tuning existing models is a good option when the foundation model partially meets the requirements but needs improvements for specific use cases. By distilling the knowledge from a larger model into a smaller one, developers can create more focused and efficient models for their specific needs.

Q: How can developers collect and label their own data for training custom models?

Developers can collect their own data by capturing images or videos relevant to their use case. They can then label the data by identifying and marking specific objects or attributes of interest in the images. This labeled data is used to train the custom model and improve its performance.

Q: What are some challenges in deploying computer vision models in real-world applications?

Real-world deployments of computer vision models may face challenges such as limited compute resources, the need for real-time processing, and the availability of proprietary or domain-specific data. Balancing performance, accuracy, and resource constraints is crucial when deploying computer vision models.

Summary & Key Takeaways

• Computer vision enables apps to process images and videos, turning them into actionable insights.

• Using existing models: Foundation models like CLIP can be used out of the box for tasks like semantic search, but may fall short in certain areas.

• Fine-tuning models: If existing models don't fully meet the requirements, distillation techniques can be used to train smaller, more specific models for improved performance.

• Training custom models: In cases where there are no existing models available, developers can curate their own data sets, label images, and train models to solve specific use cases.