merveenoyan/smol-vision resource

Health v2 · maintenance only

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1973 stars · 151 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter Notebook recipes for shrinking, optimizing, and fine-tuning cutting-edge vision and multimodal AI models. It covers quantization with ONNX/Optimum, knowledge distillation, and fine-tuning models like PaliGemma, Florence-2, and DINOv3.

Use cases

• quantize vision models to run on smaller hardware
• fine-tune a vision language model like PaliGemma
• optimize object detection models with ONNX Runtime
• learn knowledge distillation for image classification
• fine-tune Florence-2 for document question answering
• fine-tune Kosmos2.5 for OCR with bounding boxes
• export vision models to ONNX and reduce them to fp16

When to choose

• you want hands-on notebooks for optimizing or shrinking vision models
• you need recipes for fine-tuning multimodal or vision-language models
• you want to learn quantization and ONNX export for computer vision models

When to avoid

• you need a production-ready library or CLI rather than example notebooks
• you work with non-vision modalities like audio or text-only LLMs
• you need guaranteed long-term API stability since content is tutorial notebooks

Facets

learning-resource · maturity active

machine-learning image-processing ocr llm-training computer-vision machine-learning deep-learning tutorials python notebooks vision-models quantization fine-tuning multimodal knowledge-distillation onnx huggingface

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markdown · JSON · MCP: product_card(name="merveenoyan/smol-vision")