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GPT4o vs Llama 3 vs Phi3 AI vision and visual analytics compared

AI vision compared

The rise of open-source vision models has revolutionized the field of AI vision and image interpretation. Two notable examples are that of Microsoft Phi 3 vision and Meta’s Llama 3. These powerful tools are designed to perform a wide range of tasks, from generating simple image descriptions to performing complex image analyses.

If you’d like to learn more about the different AI models available and how they perform during visual analytics tests, you’ll be happy to know that Matthew Berman has conducted several tests and observations for your viewing pleasure. We compare the performance of these AI vision models with the well-known ones GPT-4 in various image interpretation tasks to assess their effectiveness and identify their strengths and weaknesses.

AI Vision image description

One of the most important tasks of vision models is to provide accurate and detailed descriptions of images. Let’s see how each model fares in this aspect:

  • Phi 3 Vision excels at providing fast and accurate descriptions. It can describe a scene in precise detail, capturing the essential elements of the image.
  • Llama 3 with Llama 3 takes a more artistic approach, offering detailed and creative descriptions that add a unique touch to the interpretations.
  • GPT-4, although slower compared to the other models, demonstrates its accuracy by correctly identifying specific objects in an image, such as a llama.

Identification of individuals

Recognizing specific individuals from images is a challenging task for vision models. In our tests, none of the models could identify Bill Gates from an image, highlighting a common limitation in this area. This indicates that further improvements are needed to improve the models’ ability to accurately recognize and identify specific individuals.

CAPTCHA recognition

CAPTCHA recognition is an important task that tests the robustness of vision models. Here’s how each model performed:

  • Phi 3 Vision successfully identified both the CAPTCHA and the letters, demonstrating his strong performance in this task.
  • Llama 3 with Llama 3 produced partially correct results, showing some capacity but not achieving full accuracy.
  • GPT-4 initially failed but succeeded on a second attempt, demonstrating its ability to learn and adapt.

Complex image descriptions

When it comes to analyzing complex images and providing detailed descriptions, the models exhibit several strengths:

  • Both Phi 3 Vision and Llama 3 with Llama 3 excel at generating rich descriptions, demonstrating their proficiency in complex image analysis.
  • GPT-4 provides accurate but less detailed descriptions, striking a balance between correctness and brevity.

Open source AI Vision models tested

Here are some other articles you might find interesting on the topic of AI vision:

iPhone storage settings

Interpreting iPhone storage settings from an image is a practical task that tests the models’ ability to extract relevant information. The results are as follows:

  • Phi 3 Vision provides accurate and detailed information about iPhone storage settings, proving its effectiveness in this area.
  • Llama 3 has difficulty providing specific details, indicating that there is a gap in performance for this particular task.
  • GPT-4 outperforms the other models and provides comprehensive and accurate details about iPhone storage settings.

Read QR code

Extracting information from QR codes is another practical application of vision models. However, all three models failed to extract the URL from a QR code, revealing a common limitation that will need to be addressed in future iterations of these models.

Meme explanation

Understanding and explaining memes requires a combination of visual perception and contextual knowledge. Let’s see how the models approach this task:

  • Phi 3 Vision gives an incorrect explanation, misses the context and does not understand the meaning of the meme.
  • Llama 3 with Llama 3 offers a descriptive explanation, but is not precise, indicating a partial understanding of the meme.
  • GPT-4 demonstrates his capabilities by providing correct and insightful explanations, demonstrating his ability to understand memes effectively.

Conversion from table to CSV

Converting tabular data from an image to CSV format is a valuable feature of view models. Here’s how each model performs:

  • Phi 3 Vision excels at this task, providing fast and accurate conversion, demonstrating its efficiency when processing structured data.
  • Llama 3 with Llama 3 fails to convert the table to CSV, indicating a limitation in its data processing capabilities.
  • GPT-4 goes one step further by creating a downloadable CSV file, demonstrating its practicality in extracting and manipulating data.

Overall performance and future testing

Based on our comparative analysis Phi 3 vision emerges as the most impressive model overall, excelling at multiple tasks and demonstrating its versatility. Llama 3 performs well initially, but has difficulty with specific tasks, indicating that there are areas for improvement. GPT-4 shows mixed results, with some tasks performed exceptionally well while others fall short.

To further evaluate the capabilities and limitations of these vision models, we recommend suggesting additional ways to test them. By expanding the range of tasks and scenarios, we can gain deeper insight into their strengths and weaknesses, which guides us in selecting the most suitable tool for specific AI image interpretation needs.

In conclusion, the emergence of open-source vision models such as Phi 3 Vision and Llama 3 with Llama 3 has opened up new possibilities in AI image interpretation. By comparing their performance against GPT-4, we can assess their effectiveness and identify areas for improvement. As these models continue to evolve, we can expect even more advanced capabilities in the future, revolutionizing the way we analyze and understand visual data.

Video credits: Source

Filed under: Technology News

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