efficient_densenet_pytorch
by gpleiss
A memory-efficient implementation of DenseNets
AI summary
DenseNet optimizer
An implementation of DenseNets optimized to save GPU memory by using checkpointing
- stars
- 1.5K
- forks
- 327
- watching
- 44
Similar projects
Found by comparing what the projects do, not just their names.
EfficientNet model
A PyTorch implementation of EfficientNet for computer vision tasks
Neural network
An implementation of a deep neural network architecture in PyTorch
Neural network optimizer
An implementation of an optimization algorithm for training neural networks in machine learning environments.
Memory optimizer for deep learning models
A lightweight wrapper around PyTorch to prevent CUDA out-of-memory errors and optimize model execution
Deep learning models
Implementations of deep learning architectures using PyTorch for image classification tasks on various datasets.
MobileNetV3 optimizer
An implementation of MobileNetV3 using PyTorch with search space optimization
Object descriptor network
This project presents a method for learning dense object descriptors from self-supervised data, enabling tasks such as robotic manipulation and object recognition.
Mobile architecture optimizer
An implementation of a neural network architecture search algorithm optimized for mobile devices.
Deep network architecture
An implementation of Wide Residual Networks in PyTorch for efficient deep learning on CIFAR10/100 datasets.
Deep learning library
A JavaScript library that provides GPU-accelerated deep learning capabilities with automatic differentiation and neural network layers.
Language Model
Trains a large-scale PyTorch language model on the 1-Billion Word dataset
Optimization layer
A PyTorch module that adds differentiable optimization as a layer to neural networks
Dense Network Architecture
An implementation of a deep learning network architecture with dense connectivity and bottleneck layers.
Deep learning toolkit
A Python framework for building deep learning models with optimized encoding layers and batch normalization.
Model optimizer
Enables the creation of smaller neural network models through efficient pruning and quantization techniques
