dbolya/yolact
Yolact
Yolact este un framework de viziune computerizată și un model de segmentare a instanțelor în timp real. Utilizează o rețea neuronală complet convoluțională pentru a detecta obiecte și a genera măști la nivel de pixel pentru imagini și fluxuri video.
Sistemul folosește generarea de măști prototipice pentru a crea prototipuri globale de măști care sunt combinate liniar pentru rezultate specifice instanței. Încorporează straturi convoluționale deformabile și pooling de regiuni de interes (RoI) deformabile pentru a adapta eșantionarea spațială la formele neregulate ale obiectelor.
Framework-ul acoperă întregul ciclu de viață al dezvoltării modelului, inclusiv antrenarea pe seturi de date personalizate, evaluarea acurateței folosind media Average Precision și utilizarea antrenării distribuite multi-GPU pentru a scala viteza de procesare. Oferă, de asemenea, utilitare de procesare media pentru aplicarea măștilor de segmentare pe imagini și exportul fișierelor video adnotate.
Proiectul include instrumente de persistență a stării pentru gestionarea checkpoint-urilor și reluarea antrenării, alături de logare pentru înregistrarea metricilor și a valorilor de pierdere (loss).
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.
