The emergence of open-source vision models has revolutionized the field of AI vision and image interpretation. Two notable examples are Microsoft’s Phi 3 Vision and Meta’s Llama 3. These powerful tools are designed to tackle a wide range of tasks, from generating simple image descriptions to performing complex image analysis.
If you would like to learn more about the different AI models available and how they perform during visual analytic test, you will be pleased to know that Matthew Berman has carried out various tests and observations for your viewing pleasure. Comparing the performance of these AI vision models against the well-known GPT-4 in various image interpretation tasks to assess their effectiveness and identify their strengths and limitations.
AI Vision Image Description
One of the primary 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 in providing fast and accurate descriptions. It can describe a scene with precise details, 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 its 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 advancements are needed to improve the models’ ability to recognize and identify specific individuals accurately.
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 its strong performance in this task.
- Llama 3 with Llama 3 provided partially correct results, showing some capability but not achieving full accuracy.
- GPT-4 initially failed but succeeded on a second attempt, showcasing its ability to learn and adapt.
Complex Image Descriptions
When it comes to analyzing complex images and providing detailed descriptions, the models exhibit different strengths:
- Both Phi 3 Vision and Llama 3 with Llama 3 excel in generating comprehensive descriptions, demonstrating their proficiency in complex image analysis.
- GPT-4 provides accurate but less detailed descriptions, striking a balance between correctness and conciseness.
Open source AI Vision models tested
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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 delivers accurate and detailed information about iPhone storage settings, showcasing its effectiveness in this area.
- Llama 3 with Llama 3 struggles to provide specific details, indicating a gap in its performance for this particular task.
- GPT-4 outperforms the other models, offering comprehensive and accurate details about the iPhone storage settings.
QR Code Reading
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 needs 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 handle this task:
- Phi 3 Vision provides an incorrect explanation, missing the context and failing to grasp the meaning of the meme.
- Llama 3 with Llama 3 offers a descriptive explanation but lacks accuracy, indicating a partial understanding of the meme.
- GPT-4 demonstrates its capability by giving a correct and insightful explanation, showcasing its ability to comprehend memes effectively.
Table to CSV Conversion
Converting tabular data from an image to a CSV format is a valuable feature of vision models. Here’s how each model performs:
- Phi 3 Vision excels in this task, providing quick and accurate conversion, demonstrating its efficiency in handling structured data.
- Llama 3 with Llama 3 fails to convert the table to CSV, indicating a limitation in its data handling capabilities.
- GPT-4 goes a step further by creating a downloadable CSV file, showcasing its practical utility in data extraction and manipulation.
Overall Performance and Future Tests
Based on our comparative analysis, Phi 3 Vision emerges as the most impressive model overall, excelling in multiple tasks and demonstrating its versatility. Llama 3 performs well initially but struggles with specific tasks, indicating 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 encourage you to suggest additional ways to test them. By expanding the range of tasks and scenarios, we can gain deeper insights into their strengths and weaknesses, guiding us in selecting the most suitable tool for specific AI image interpretation needs.
In conclusion, the emergence of open-source vision models like 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.
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