DePlot: Visual Language Reasoning Model
| Property | Value |
|---|---|
| Parameter Count | 282M |
| License | Apache 2.0 |
| Paper | arXiv:2212.10505 |
| Languages Supported | English, French, Romanian, German, Multilingual |
| Architecture | Pix2struct-based Transformer |
What is DePlot?
DePlot is a groundbreaking visual language model developed by Google that introduces a novel one-shot approach to visual language reasoning. The model specializes in understanding and analyzing charts and plots by decomposing the complex task into two manageable steps: plot-to-text translation and reasoning over the translated text.
Implementation Details
The model utilizes a Pix2struct architecture with 282M parameters, implemented in PyTorch with Safetensors support. It works by first converting visual plot data into a linearized table format, which can then be processed by Large Language Models (LLMs) for reasoning tasks.
- Built on Transformer architecture with visual understanding capabilities
- Supports F32 tensor operations
- Implements visual question-answering pipeline
- Provides multilingual support across 5 languages
Core Capabilities
- One-shot visual language reasoning
- Plot and chart comprehension
- Automatic data table generation from visual inputs
- 24.0% improvement over previous SOTA on human-written queries
- Multilingual support for diverse applications
Frequently Asked Questions
Q: What makes this model unique?
DePlot's distinctive feature is its ability to perform one-shot visual language reasoning without requiring extensive training data, unlike previous models that needed tens of thousands of examples. It achieves this through its innovative two-step approach of plot-to-text translation followed by LLM reasoning.
Q: What are the recommended use cases?
The model is ideal for applications involving chart and plot analysis, automated data extraction from visualizations, and visual question-answering systems. It's particularly useful in scenarios requiring multilingual support and where traditional methods would require extensive training data.
