notebooks: Learn and Apply Computer Vision Models

Overview

roboflow/notebooks is a collection of Jupyter tutorials that demonstrate computer vision models and workflows, from image classification and object detection to segmentation, pose estimation, OCR, and visual data extraction. It helps learners and practitioners move from a model or technique to a runnable example without assembling every tutorial from scratch.

Many notebooks link to Colab and Kaggle, with related model repositories, papers, videos, and articles where available. The collection spans both foundational architectures and newer vision and vision-language models, making it useful for comparing approaches and adapting examples to custom datasets.

Key Features

• Tutorials for image classification, object detection, segmentation, pose estimation, tracking, OCR, and data extraction.
• Examples featuring models such as YOLO, RF-DETR, SAM, Florence-2, PaliGemma, and Qwen vision-language models.
• Fine-tuning guides for custom datasets across multiple model families.
• Zero-shot and open-vocabulary detection and segmentation examples.
• Hosted notebook links for running many tutorials in Google Colab or Kaggle.
• Links to related model repositories, research papers, videos, and Roboflow articles where provided.

Use Cases

• A computer vision learner wants a practical walkthrough of a model or task before studying its implementation in depth.
• An engineer needs a starting point for fine-tuning a supported model on a custom detection, classification, or segmentation dataset.
• A researcher wants to try zero-shot or open-vocabulary vision techniques using a runnable notebook.
• A team evaluating OCR, tracking, pose estimation, or vision-language workflows wants examples to adapt for a prototype.

Getting Started

Open the repository, choose a tutorial under notebooks/, and follow its Colab or Kaggle link when available. See the README for the tutorial catalog and related materials.

Alternatives

• pytorch-deep-learning: Offers a structured, general PyTorch deep-learning course, while notebooks focuses on hands-on computer-vision tutorials and examples.

Considerations

• The repository is a tutorial collection, not a single installable computer vision package. Dependencies and setup may differ between notebooks.
• Notebook execution may require a compatible runtime, model downloads, credentials, or GPU resources, depending on the selected model and workflow.
• Examples cover many model families, so check each notebook and its linked upstream repository for current requirements and licensing terms.
• No license is specified in the provided repository information.