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.
Pip install the multimodal-maestro package in a 3.11>=Python>=3.8 environment.
pip install multimodal-maestro
Find dog.
>>> The dog is prominently featured in the center of the image with the label [9].
👉 read more
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load image
import cv2 image = cv2.imread("...")
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create and refine marks
import multimodalmaestro as mm generator = mm.SegmentAnythingMarkGenerator(device='cuda') marks = generator.generate(image=image) marks = mm.refine_marks(marks=marks)
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visualize marks
mark_visualizer = mm.MarkVisualizer() marked_image = mark_visualizer.visualize(image=image, marks=marks)
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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]."
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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]]) ... }
- Documentation page.
- Segment Anything guided marks generation.
- Non-Max Suppression marks refinement.
- LLaVA demo.
- Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V by Jianwei Yang, Hao Zhang, Feng Li, Xueyan Zou, Chunyuan Li, Jianfeng Gao.
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.