microsoft/computervision-recipes

Features

• Computer Vision Tutorials - Offers a comprehensive collection of code samples and best practices for building deep learning image and video analysis models.
• Implementation Recipes - Organizes complex computer vision tasks into modular recipes and guided workflows for reproducible model building.
• Object Detection and Tracking - Provides implementations for identifying objects via bounding boxes and tracking their movement across video frames.
• Object Detection - Locates and identifies items within an image by generating bounding boxes and class labels.
• Image Segmentation - Provides techniques for partitioning images into precise object boundaries and masks.
• Segmentation Model Training - Develops segmentation systems by fine-tuning pre-trained backbones on custom annotated datasets.
• Image Classification - Implements supervised machine learning techniques to assign category labels to images.
• Classification Training - Provides implementations for building single or multi-label image classification models.
• Fine-Tuning Pipelines - Implements workflows for adapting pre-trained neural network backbones to custom datasets.
• Vision Workflows - Provides Python-based guides for training, fine-tuning, and validating neural networks for visual data processing.
• Action Recognition Training - Builds and evaluates models for activity classification using custom datasets or benchmark fine-tuning.
• Model Recipes - Provides modular, step-by-step implementations for common vision tasks like object detection and action recognition.
• Notebook Execution Environments - Uses notebook-driven prototyping to iteratively develop and validate computer vision algorithms.
• Image Similarity Estimation - Implements a framework for computing visual similarity and executing fast image retrieval.
• Model Deployment - Provides a workflow for packaging trained vision models into containers for scalable inference.
• Object Pose Estimations - Locates anatomical keypoints on the human body to determine posture and orientation.
• Representation Learning - Trains deep neural networks to compute image representations for identifying similar images.
• Crowd - Provides tools to estimate human density and count individuals within varied scene environments.
• Human Activity Recognition - Analyzes video sequences to identify, categorize, and timestamp specific human activities.
• Image Retrieval Systems - Executes rapid searches for similar images using nearest neighbor search algorithms.
• Keypoint Detection - Identifies points of interest on objects using models that detect both the object and its keypoints.
• Training Pipelines - The computer vision library builds custom models to localize specific points of interest on objects using a mask-based framework.
• Model Fine-Tuning - The computer vision library adjusts pre-trained models using custom datasets to improve accuracy for specific tasks.
• Vision Model Training - The computer vision library trains high-accuracy models for identifying and locating objects in custom datasets.
• Hyperparameter Optimization - Utilizes grid search and parallel sweeping to find optimal model parameters for accuracy and speed.
• Retrieval Re-ranking - Implements k-reciprocal re-ranking to refine the accuracy of image retrieval results.
• Hard Negative Mining - Increases model precision by sampling difficult negative examples during the training process.
• Video Object Tracking - Identifies and follows multiple distinct objects across video frames using tracking algorithms.
• GPU Provisioning Services - Automates the provisioning of GPU-enabled virtual machines pre-configured with necessary vision libraries.
• Containerized Training Environments - Creates containerized images with standardized dependencies for CPU and GPU-based model training and testing.
• Kubernetes Deployments - Provides configurations for orchestrating containerized vision models on Kubernetes for REST API inference.
• Kubernetes Application Deployments - Packages trained vision models into containers for automated deployment to managed Kubernetes clusters.
• Model Inference APIs - Hosts trained models on cloud containers to expose them as REST APIs for scalable inference.

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