A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.

MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.

A collection of guides and examples for the Gemma open models from Google.

streamline the fine-tuning process for multimodal models: PaliGemma 2, Florence-2, and Qwen2.5-VL

VL-JEPA (Vision-Language Joint Embedding Predictive Architecture) in MLX

Testing and evaluating the capabilities of Vision-Language models (PaliGemma) in performing computer vision tasks such as object detection and segmentation.

vision language models finetuning notebooks & use cases (Medgemma - paligemma - florence .....)

使用LLaMA-Factory微调多模态大语言模型的示例代码 Demo of Finetuning Multimodal LLM with LLaMA-Factory

Minimalist implementation of PaliGemma 2 & PaliGemma VLM from scratch

Use PaliGemma to auto-label data for use in training fine-tuned vision models.

Segmentation of water in Satellite images using Paligemma

This project demonstrates how to fine-tune PaliGemma model for image captioning. The PaliGemma model, developed by Google Research, is designed to handle images and generate corresponding captions.

Rust implementation of Google Paligemma with Candle

PyTorch implementation of PaliGemma 2

PaliGemma FineTuning

Fine Tuning PaliGemma

PaliGemma Inference and Fine Tuning

Notes for the Vision Language Model implementation by Umar Jamil

PyTorch implementation of Google’s Paligemma VLM with SigLip image encoder, KV caching, Rotary embeddings and Grouped Query attention . Modular, research-friendly, and easy to extend for experimentation.

This repository contains code for fine-tuning Google's PaliGemma vision-language model on the Flickr8k dataset for image captioning tasks