Med-Flamingo provides the codebase for a multimodal medical few-shot learner, enabling rapid adaptation to new medical vision-language tasks with minimal data. It is designed for researchers and practitioners in medical AI and computer vision.

How It Works

Med-Flamingo builds upon the Flamingo architecture, integrating a vision encoder with a large language model (LLM) to process interleaved image and text data. This allows the model to learn from a few examples, making it efficient for specialized medical domains where large annotated datasets are scarce.

Quick Start & Requirements

• Install dependencies via source install.sh.
• Requires a GPU with CUDA.
• Manual download and configuration of Llama-7B (v1) model is recommended. Update tokenizer_config.json with "tokenizer_class": "LlamaTokenizer".
• Demo script available at scripts/demo.py.

Highlighted Details

• Codebase for the Med-Flamingo paper.
• Leverages the Flamingo architecture for few-shot learning.
• Supports interleaved image and text processing.

Maintenance & Community

No specific community channels or maintenance details are provided in the README.

Licensing & Compatibility

The README does not specify a license. It cites the OpenFlamingo project, which is under a permissive license. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The project is presented as a research codebase with "More updates to follow soon!", indicating it may be in early development or subject to significant changes. Specific limitations or unsupported features are not detailed.