maestro 0.1.0

Visual Prompting for Large Multimodal Models (LMMs)

multimodal-maestro

👋 hello

Multimodal-Maestro gives you more control over large multimodal models to get the outputs you want. With more effective prompting tactics, you can get multimodal models to do tasks you didn't know (or think!) were possible. Curious how it works? Try our HF space!

🚧 The project is still under construction and the API is prone to change.

💻 install

⚠️ Our package has been renamed to maestro. Install package in a 3.11>=Python>=3.8 environment.

pip install maestro

🚀 examples

GPT-4 Vision

Find dog.

>>> The dog is prominently featured in the center of the image with the label [9].

👉 read more

• load image

  import cv2
  
  image = cv2.imread("...")
  

• create and refine marks

  import maestro as mm
  
  generator = mm.SegmentAnythingMarkGenerator(device='cuda')
  marks = generator.generate(image=image)
  marks = mm.refine_marks(marks=marks)
  

• visualize marks

  mark_visualizer = mm.MarkVisualizer()
  marked_image = mark_visualizer.visualize(image=image, marks=marks)
  

  

• prompt

  prompt = "Find dog."
  
  response = mm.prompt_image(api_key=api_key, image=marked_image, prompt=prompt)
  

  >>> "The dog is prominently featured in the center of the image with the label [9]."
  

• extract related marks

  masks = mm.extract_relevant_masks(text=response, detections=refined_marks)
  

  >>> {'6': array([
  ...     [False, False, False, ..., False, False, False],
  ...     [False, False, False, ..., False, False, False],
  ...     [False, False, False, ..., False, False, False],
  ...     ...,
  ...     [ True,  True,  True, ..., False, False, False],
  ...     [ True,  True,  True, ..., False, False, False],
  ...     [ True,  True,  True, ..., False, False, False]])
  ... }
  

🚧 roadmap

• Documentation page.
• Segment Anything guided marks generation.
• Non-Max Suppression marks refinement.
• LLaVA demo.

💜 acknowledgement

• Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V by Jianwei Yang, Hao Zhang, Feng Li, Xueyan Zou, Chunyuan Li, Jianfeng Gao.

🦸 contribution

We would love your help in making this repository even better! If you noticed any bug, or if you have any suggestions for improvement, feel free to open an issue or submit a pull request.