This project is a collection of educational resources and implementation frameworks providing deep learning model recipes, code samples, and step-by-step guides for computer vision tasks. It organizes complex workflows into modular recipes and implementation guides to facilitate the building of image and video analysis models.
The framework focuses on specialized vision capabilities, including an image similarity framework for fast retrieval and re-ranking, human pose estimation, and video action recognition. It also provides specific tools for crowd density estimation and document image cleaning.
The project covers a broad range of development and deployment capabilities, including image classification, object detection, and image segmentation. It provides utilities for data annotation, model training with hyperparameter optimization, and the orchestration of models using containers and Kubernetes for REST API inference.
The implementation is centered around a PyTorch vision workflow using notebook-driven prototyping.
