KaimingHe/deep-residual-networks
Deep Residual Networks
Ce projet fournit un framework de réseau résiduel profond et des modèles PyTorch pré-entraînés conçus pour la reconnaissance d'images haute précision. Il implémente une architecture de réseau de neurones qui utilise des connexions de saut (skip connections) pour permettre l'entraînement de modèles très profonds sans dégradation du gradient.
Le système est conçu pour des tâches de vision par ordinateur, incluant la classification d'images, la détection d'objets et la segmentation de données visuelles. Il inclut des poids entraînés sur ImageNet pour soutenir le transfert learning et le fine-tuning de modèles sur des datasets d'images personnalisés.
La conception architecturale se concentre sur les blocs d'apprentissage résiduel, les configurations de couches goulot d'étranglement (bottleneck) et la normalisation par lots pour maintenir la stabilité pendant l'entraînement. Le framework emploie également le pooling moyen global pour réduire les paramètres et prévenir le surapprentissage.
Features
- Residual Networks - Implements a deep residual network architecture using skip connections to enable extreme depth without gradient degradation.
- Computer Vision Models - Provides a comprehensive framework of neural network architectures for image classification, detection, and feature extraction.
- Image Classification Models - Provides ResNet-based image classification models that utilize shortcut connections for high-accuracy visual recognition.
- Convolutional Neural Network Training - Provides a framework for training deep convolutional neural networks to solve complex image classification and detection problems.
- Image Classification - Implements high-accuracy image classification using deep residual networks to assign categories to visual content.
- Residual Block Composers - Implements residual learning blocks that allow the network to learn additive differences between input and output.
- Skip-Connection Architectures - Uses skip-connection identity mapping to bypass layers, preventing gradient degradation in very deep architectures.
- Batch Normalization - Implements batch normalization to stabilize training and accelerate convergence in deep residual networks.
- Object Detection - Supports object detection tasks by locating and identifying multiple specific objects within a visual frame.
- Pooling Replacements - Utilizes global average pooling to replace fully connected layers, reducing total parameters and preventing overfitting.
- Bottleneck Layers - Employs bottleneck layers to reduce dimensionality and computational overhead through 1x1 and 3x3 convolutions.
- Convolutional Stage Stacking - Arranges multiple residual blocks in sequential stages to increase the depth of the feature extraction process.
- Model Fine-Tuning - Allows the adaptation of pre-trained network weights to new datasets through targeted fine-tuning.
- Pre-trained Model Zoos - Ships pre-trained PyTorch weights trained on ImageNet to serve as a starting point for fine-tuning.
- Image Segmentations - Provides deep residual network models capable of partitioning images into precise foreground and background segments.
- Semantic Image Segmentations - Divides images into precise segments to isolate objects and patterns for detailed analysis.
- Transfer Learning - Facilitates transfer learning by adapting pre-trained ImageNet weights to custom datasets for improved accuracy.
- Vision par ordinateur - Deep residual learning for image recognition.
- Computer Vision Libraries - Deep residual learning for image recognition.
- Perception Models - Deep residual learning framework for image recognition.
- CNN - Listed in the “CNN” section of the Ailia Models awesome list.
