dbolya/yolact
Yolact
Yolact is a computer vision framework and real-time instance segmentation model. It utilizes a fully convolutional neural network to detect objects and generate pixel-level masks for images and video feeds.
The system employs prototypical mask generation to create global mask prototypes that are linearly combined for instance-specific results. It incorporates deformable convolutional layers and deformable region-of-interest pooling to adapt spatial sampling to the irregular shapes of objects.
The framework covers the full model development lifecycle, including training on custom datasets, accuracy evaluation using mean Average Precision, and the use of multi-GPU distributed training to scale processing speed. It also provides media processing utilities for applying segmentation masks to images and exporting annotated video files.
The project includes state persistence tools for checkpoint management and training resumption, alongside logging for recording metrics and loss values.
Features
- Segmentation Model Training - Provides a full framework for training real-time instance segmentation models to detect and mask objects.
- Fully Convolutional Architectures - Employs a fully convolutional architecture to produce pixel-level masks and bounding boxes without fully connected layers.
- Region Pooling - Extracts feature maps by pooling data according to the geometry of detected bounding boxes.
- Deformable ROI Pooling - Extracts feature maps from regions of interest by dynamically adjusting the pooling grid to match object geometry.
- Instance Segmentation Engines - Provides a high-performance engine for generating pixel-level masks to isolate individual object instances.
- Computer Vision Training Frameworks - Provides a framework for building and training neural networks specifically for object detection and segmentation.
- Real-Time Instance Segmentation - Detects and masks individual objects in images or video feeds in real-time using a fully convolutional network.
- Prototypical Mask Generators - Generates global mask prototypes that are linearly combined to produce final instance-specific segmentation masks.
- Convolutional Neural Networks - Utilizes a deep learning architecture with convolutional layers to extract spatial features from images.
- Detection Model Training - Trains models on custom or standard datasets to recognize and localize specific object classes.
- Image Annotation Integration - Supports integrating custom image datasets and annotations for training on domain-specific objects.
- Deformable Convolutions - Implements convolutional layers with learnable offsets to better fit the irregular shapes of objects.
- Real-Time Instance Segmentation Models - Implements a fully convolutional neural network that generates pixel-level masks in real-time.
- Non-Maximum Suppression - Removes redundant bounding boxes using suppression algorithms to ensure each object is detected only once.
- Checkpoint Management - Implements state persistence for saving and selecting optimal model weights during the training process.
- Computer Vision Pipelines - Implements an automated workflow to process media files for object identification and pixel-level masking.
- Distributed GPU Training - Distributes the computational load of neural network training across multiple graphics cards to accelerate convergence.
- Model Evaluation Metrics - Calculates precision metrics such as mean Average Precision to validate model accuracy on image datasets.
- Training Resumption - Enables restoring a model from a saved checkpoint to continue training from a specific iteration.
- Multi-GPU Training Utilities - Scales batch sizes and processing speed across multiple graphics cards via a distributed training pipeline.
- Detection Accuracy Evaluators - Calculates mean Average Precision to quantify the localization and classification accuracy of object detection.
- Performance Validators - Evaluates model precision and mean average precision against validation subsets during training.
- Computer Vision - Real-time instance segmentation model.
- Computer Vision and Image Processing - Real-time instance segmentation model.
- Computer Vision Libraries - Real-time instance segmentation model.
- Segmentation Architectures - YOLACT real-time instance segmentation in PyTorch.
- CNN - Listed in the “CNN” section of the Ailia Models awesome list.
Star history
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