GIT-large-coco
| Property | Value |
|---|---|
| Parameter Count | 394M |
| License | MIT |
| Paper | View Paper |
| Author | Microsoft |
| Framework | PyTorch |
What is git-large-coco?
GIT-large-coco is a Generative Image-to-Text Transformer model developed by Microsoft, specifically designed for vision-language tasks. It's a large-sized variant fine-tuned on the COCO dataset, implementing a transformer decoder architecture that processes both CLIP image tokens and text tokens to generate descriptive text from images.
Implementation Details
The model utilizes a sophisticated architecture combining bidirectional attention for image patch tokens and causal attention for text tokens. It was trained on 20 million image-text pairs and further fine-tuned on COCO dataset, making it particularly effective for image captioning tasks.
- Leverages CLIP image token conditioning
- Implements teacher forcing during training
- Utilizes both bidirectional and causal attention mechanisms
- Supports SafeTensors format
Core Capabilities
- Image captioning
- Visual question answering (VQA)
- Image classification through text generation
- Video captioning support
Frequently Asked Questions
Q: What makes this model unique?
The model's architecture combines CLIP image tokens with text tokens in a novel way, using different attention mechanisms for images and text. This allows for more nuanced image understanding and text generation.
Q: What are the recommended use cases?
The model excels in image captioning tasks and can be effectively used for visual question answering. It's particularly suitable for applications requiring detailed image descriptions or interactive visual understanding tasks.
