Open-Source Vision AI Performance

In the video titled “Open-Source Vision AI – SURPRISING Results! (Phi3)”, Matthew Berman explores the capabilities of two new open-source vision models: Phi3 Vision and LLaMA 3 Vision. The protagonist begins by emphasizing the rapid advancements in vision models, particularly with the introduction of GPT-4, which excels at interpreting images. He sets up a comparison between Phi3 Vision, created by Microsoft, and LLaMA 3 Vision, developed by Meta, using a series of tests to evaluate their performance. The video features three screens displaying the outputs of each model as they analyze various images. The first test involves describing a simple image of an alpaca, where both Phi3 Vision and LLaMA 3 Vision provide detailed descriptions, with LLaMA 3’s output being more artistic. However, when asked to identify a famous figure, both models decline to specify who it is, while GPT-4 also struggles with personal identification. The protagonist then tests the models on their ability to read text from images, with Phi3 Vision performing best by accurately identifying both the letters in a capture and the word ‘capture’ itself. As the tests progress, the protagonist evaluates the models’ descriptions of various images, including a screenshot of an iPhone storage setting and a meme contrasting startups and big companies. While Phi3 Vision demonstrates strong performance throughout, LLaMA 3 Vision struggles with specific tasks, often providing generic descriptions instead of direct answers. Ultimately, the video concludes with the protagonist declaring Phi3 Vision the winner for its consistent and accurate results, while inviting viewers to suggest further tests for these models. This engaging presentation highlights the potential of open-source vision AI tools in various applications, encouraging viewers to explore their capabilities.