This repository provides a comprehensive collection of over 70 Jupyter notebooks demonstrating state-of-the-art computer vision models and techniques. It targets researchers, engineers, and practitioners looking to implement and fine-tune models for tasks like object detection, segmentation, and data extraction, offering practical examples for rapid adoption.

How It Works

The project curates and presents tutorials for a wide array of popular computer vision models, including YOLO variants, SAM, Florence-2, and multimodal models like Qwen2.5-VL. Each notebook is designed to be runnable in cloud environments like Google Colab, Kaggle, or SageMaker Studio Lab, facilitating easy experimentation and learning without complex local setup. The tutorials cover both foundational concepts and advanced applications, such as zero-shot learning and fine-tuning for specific data extraction tasks.

Quick Start & Requirements

• Install/Run: Clone the repository and run jupyter notebook after installing dependencies within a virtual environment.
• Prerequisites: Python 3.x, Jupyter Notebook.
• Resources: Cloud environments like Colab, Kaggle, or SageMaker Studio Lab are recommended for ease of use.
• Links: notebooks

Highlighted Details

• Covers 50+ model tutorials for object detection, segmentation, pose estimation, and OCR.
• Includes 2 tracker tutorials for SORT and DeepSORT.
• Features 21 computer vision skills tutorials, including auto-annotation and speed estimation.
• Provides links to complementary materials, repositories, and papers for each tutorial.

Maintenance & Community

The project encourages community contributions for new tutorials and bug reports. Users can find information on contributing via a dedicated guide.

Licensing & Compatibility

The repository itself is not explicitly licensed in the README. However, the underlying models and libraries used within the notebooks will have their own respective licenses, which users must adhere to. Compatibility for commercial use depends on the licenses of the individual models and frameworks demonstrated.

Limitations & Caveats

Notebooks may occasionally lag behind rapidly evolving library updates, requiring users to report bugs or adapt code. The project does not provide a unified inference API or pre-trained models; it serves as a collection of educational examples.