matterport/Mask_RCNN

Features

• Object Detection - Provides a comprehensive system for identifying and isolating individual objects using both bounding boxes and pixel-level masks.
• Region Alignment - Uses bilinear interpolation to align regions of interest, ensuring precise spatial alignment for pixel-level mask prediction.
• Instance Segmentation Engines - Provides an engine that generates precise pixel-level masks to isolate individual object instances within images.
• Image Segmentation - Provides a complete workflow for partitioning images into distinct regions and object masks for visual analysis.
• Binary Mask Generators - Implements a parallel convolutional branch to generate binary masks for isolating individual object instances at the pixel level.
• Detection Model Training - Provides a framework for training object detection and instance segmentation models on custom datasets.
• Detection Pipelines - Implements a two-stage neural pipeline that sequentially handles region proposals followed by classification and masking.
• Object Detection Models - Provides a toolset of neural network architectures designed to identify and locate multiple object instances in images.
• Custom Vision Training - Enables fine-tuning of pre-trained vision models on specialized user-provided datasets to recognize new object categories.
• Mask R-CNN Implementations - Provides a full TensorFlow and Keras implementation of the Mask R-CNN architecture.
• Region Proposal Networks - Employs a region proposal network to identify candidate object locations before refining them into bounding boxes.
• TensorFlow Model Development - Offers a framework for designing and training instance segmentation models using the TensorFlow ecosystem.
• Vision Model Fine-Tuning - Allows adapting pre-trained vision models to new datasets using specialized transfer scripts to improve detection for new classes.
• Custom Dataset Integration Methods - Provides mechanisms to integrate user-provided image data through custom configuration and dataset loading classes.
• Distributed GPU Training - Includes capabilities for distributed GPU training to accelerate the optimization of large vision models.
• Distributed Training - Distributes the computational load of model training across multiple GPUs to accelerate optimization.
• Runtime Activation Visualizations - Includes diagnostics for internal model inspection through the visualization of layer activations.
• Keras Model Implementations - Implements neural network layers and training scripts specifically using the Keras API for image recognition.
• Pretrained Weight Initializers - Provides utilities to initialize training using pre-trained weights from large datasets to accelerate convergence.
• Visual Debugging Outputs - Renders intermediate outputs of the detection and segmentation process to analyze pipeline performance.
• Deep Learning Execution - Executes deep learning operations through a TensorFlow computational graph to optimize tensor flow across CPU and GPU hardware.
• Activation Visualizations - Offers diagnostics for internal model inspection, including the visualization of layer activations and anchor filtering.

Star history