deplot
Maintainer: google
203
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The deplot model is a powerful tool developed by Google that aims to revolutionize the way we interact with visual data such as charts and plots. Unlike previous state-of-the-art models that require extensive training on thousands of examples, deplot takes a novel approach by decomposing the challenge of visual language reasoning into two steps: (1) plot-to-text translation and (2) reasoning over the translated text. This one-shot solution leverages the few-shot reasoning capabilities of large language models (LLMs) to achieve significant improvements in understanding human-written queries related to chart analysis.
The key component of deplot is a modality conversion module that translates the image of a plot or chart into a linearized table. This output can then be directly used to prompt a pre-trained LLM, allowing it to exploit its powerful reasoning abilities. This innovative approach sets deplot apart from traditional models, which often struggle with complex human-written queries.
Similar models like gfpgan, pixart-xl-2, llava-13b, thinkdiffusionxl, and sdxl focus on different aspects of image-to-text or text-to-image generation, but deplot stands out with its unique approach to visual language reasoning.
Model inputs and outputs
Inputs
- Image: The image of a chart or plot that the model will process and translate to text.
- Text: A human-written query or question about the information contained in the chart or plot.
Outputs
- Linearized table: The output of the modality conversion module, which translates the input image into a tabular format that can be readily used to prompt a large language model.
- Answers: The response generated by the LLM based on the linearized table, addressing the original human-written query or question.
Capabilities
The deplot model excels at comprehending complex visual data and answering human-written queries about charts and plots. By bridging the gap between image and text, deplot allows users to leverage the powerful reasoning capabilities of LLMs to gain insights from visual data. This approach significantly outperforms previous state-of-the-art models, especially on challenging, human-written queries.
What can I use it for?
The deplot model can be employed in a variety of applications where the understanding of visual data is crucial. Some potential use cases include:
- Data analysis and visualization: Researchers, analysts, and data scientists can use
deplotto quickly extract insights from complex charts and plots, enabling more efficient data exploration and decision-making. - Automated report generation: Businesses can leverage
deplotto generate summaries and insights from visual data, streamlining the creation of reports and presentations. - Educational applications: Educators can use
deplotto help students better comprehend and analyze visual information, enhancing their learning experience.
Things to try
One interesting aspect of the deplot model is its ability to handle a wide range of chart types and formats. Try experimenting with different types of visualizations, such as line charts, scatter plots, and bar graphs, to see how the model performs. Additionally, you can explore the model's capabilities in answering open-ended, human-written questions about the data presented in the charts, pushing the boundaries of visual language reasoning.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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