PaliGemma: Google's Open-Source Vision-Language Model 198

PaliGemma is a vision-language model (VLM) with multimodal capabilities developed and released by Google. Unlike other VLMs (such as Openai's GPT-4o, Google's Gemini, and Anthropic's Claude 3), PaliGemma is versatile and capable of being fine-tuned on specific tasks for better performance.

PaliGemma, released alongside other products at the 2024 Google I/O event, is a multimodal model based on a combination of two other models from Google research: SigLIP (vision model) and Gemma (large language model), which means the model is a combined Transformer decoder and Vision Transformer image encoder. It takes images and text as input and generates text as output, and supports multiple languages.

What can PaliGemma do?

PaliGemma is a single-turn vision-language model that works best when fine-tuned for specific use cases. This means you can input an image and a text string, such as a prompt or question asking to caption the image, and PaliGemma will output text in response to the input, such as a caption for the image, the answer to a question, or a list of object bounding box coordinates.

PaliGemma is well-suited for tasks related to the results of the following task benchmarks released by Google:

• Fine-tuning on a single task

• Image question answering and captioning

• Video question answering and captioning

• Segmentation

This means PaliGemma is useful for simple and specific questions related to visual data.

We created a table to show how PaliGemma compares to other models based on results reported on common benchmarks.

While benchmarks are useful data points, they don't tell the whole story. PaliGemma is designed to be fine-tuned, and other models are closed-source. To show what options are available, we compare it with other models that cannot be fine-tuned (usually larger ones).

It's worth experimenting to see whether fine-tuning with custom data delivers better performance for your specific use case than the out-of-the-box performance of other models.

Later in this article, we will compare PaliGemma to other open-source VLMs and LMMs using a set of standard tests. Read on to learn about its performance.

How to Fine-Tune PaliGemma

One of the exciting aspects of PaliGemma is its ability to be fine-tuned on custom use-case data. The notebooks published by the Google PaliGemma team show how to fine-tune on a small dataset.

It should be noted that in this example, only the attention layers were fine-tuned, so performance improvements may be limited.

PaliGemma Applications

Whether using PaliGemma zero-shot or fine-tuning with custom data, there are specific use cases tailored to PaliGemma's strengths that will open the door to new AI use cases. Let's look at two of them.

Custom Applications

Models like Claude 3, Gemini 1.5 Pro, and GPT-4o can be used out of the box and applied to problems they are well-suited for. PaliGemma brings multimodal capabilities to use cases that closed models still cannot solve, because you can fine-tune PaliGemma with proprietary data relevant to your problem. This is useful in industries like manufacturing, consumer products, healthcare, and security. If you encounter a unique problem that closed models have not seen and, due to their proprietary nature, never will, PaliGemma is a great starting point for building custom AI solutions.

OCR

As shown earlier in this article, PaliGemma is a powerful OCR model without any additional fine-tuning. When building an OCR application to scale to billions of predictions, latency, cost, and accuracy can be difficult to balance. Before PaliGemma, closed-source models were the best-performing options of their kind, but their cost and lack of model ownership made them hard to justify in production. This model can deliver immediate performance and improve over time by being fine-tuned on specific data.