GIT-Base: Generative Image-to-Text Transformer
| Property | Value |
|---|---|
| Parameter Count | 177M parameters |
| License | MIT |
| Author | Microsoft |
| Paper | View Research Paper |
| Downloads | 2,035,267 |
What is git-base?
GIT-base is Microsoft's implementation of a Transformer decoder model designed for converting images to text. It represents a base-sized version of the GIT architecture, trained on 10 million image-text pairs. The model uniquely combines CLIP image tokens with text tokens, enabling sophisticated image understanding and text generation capabilities.
Implementation Details
The model employs a transformer architecture with bidirectional attention for image tokens and causal attention for text tokens. It processes images using CLIP-based tokenization and generates text through an autoregressive approach.
- Utilizes both image patch tokens and text tokens for prediction
- Implements teacher forcing during training
- Processes image inputs through CLIP tokenization
- Supports PyTorch framework with Safetensors compatibility
Core Capabilities
- Image and video captioning
- Visual question answering (VQA)
- Image classification through text generation
- Cross-modal understanding between vision and language
Frequently Asked Questions
Q: What makes this model unique?
GIT-base stands out for its efficient architecture that combines CLIP image understanding with generative text capabilities, all while maintaining a relatively compact size of 177M parameters. It's particularly notable for its flexible application across multiple vision-language tasks.
Q: What are the recommended use cases?
The model is best suited for image captioning tasks, visual question answering, and scenarios requiring natural language descriptions of visual content. It's particularly valuable in applications requiring detailed image understanding and natural language generation.
