ai-agent

Monet

[CVPR 2026] Official codes of "Monet: Reasoning in Latent Visual Space Beyond Image and Language"
232 stars+3★ this week12 forksPythonUpdated 3/19/2026100% free · open source
What it does

Monet lets you run reasoning tasks (e.g., visual question answering, image‑based commonsense inference) directly in the latent space of a pretrained vision model, without needing separate language prompts.

When to use it
  • •You need to answer complex questions about images that go beyond simple captioning (e.g., “Is the person likely to be late for work?”).
  • •You want to prototype a visual‑reasoning feature for a product demo without training a full multimodal model from scratch.
  • •You have a dataset of images and structured queries and need a fast, zero‑shot baseline to benchmark against.
Ready-to-paste prompt
python run_query.py --image data/raw/office.jpg --question "Is the person about to miss their meeting?"
Heads up: Monet requires a CUDA‑enabled GPU with PyTorch ≥1.13; on CPU it will raise a runtime error. Make sure your environment’s `torch` version matches the CUDA toolkit installed, and that you have at least 8 GB of GPU memory for the default CLIP‑ViT‑L/14 backbone.
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Claude Code
mkdir -p ~/.claude/skills/novaglow646-monet && curl -fsSL https://workflowstacks.com/api/skills/novaglow646-monet/claude-skill -o ~/.claude/skills/novaglow646-monet/SKILL.md
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What's inside — free to inspect

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3
top-level files
8
folders
59.7M
source size
—
license
Key files
README.md
requirements.txt
File tree
deepspeed/
images/
inference/
monet_qwen_model/
RL/
script_examples/
src/
transformers/
.gitignore
README.md
requirements.txt
Quick Actions
Details
Creator
NOVAglow646
Language
Python
Category
ai-agent
Published
11/21/2025

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