huawei-noah/ghostnet
Ghostnet
GhostNet provides a set of efficient AI model architectures and neural network design patterns designed to reduce computation and memory overhead. It serves as a computer vision backbone and a lightweight vision transformer, optimizing the balance between predictive accuracy and inference speed.
The project focuses on reducing resource consumption for deployment on mobile devices and edge hardware. It achieves this through the use of lightweight vision transformer implementations and architectures that minimize the total number of parameters.
The codebase covers a range of capabilities for inference optimization, including the reduction of computational costs and memory usage. It implements structural design patterns such as depthwise-separable convolutional blocks, linear-bottleneck depthwise convolutions, and tied-weight transformer blocks to lower inference latency.
Features
- Vision Backbones and Classification - Provides high-efficiency computer vision backbones for image recognition and feature extraction.
- Depthwise Separable Convolutions - Provides depthwise-separable convolutional blocks that split standard convolutions into spatial depthwise and pointwise channel mixing filters.
- Cheap-Operation-Based Convolutions - Implements convolutional layers that use linear transformations to generate redundant feature maps and reduce floating point operations.
- Inference Cost Optimizations - Employs pre-defined neural network architectures that minimize computational costs on resource-limited hardware.
- Linear Bottlenecks - Implements linear-bottleneck depthwise convolutions to generate more feature maps with fewer parameters.
- Edge AI Model Deployment - Provides lightweight neural network architectures optimized for efficient deployment on local hardware and edge devices.
- Resource-Constrained Optimizations - Structures neural network backbones to minimize memory access and compute cycles for low-power edge hardware deployment.
- Inference Latency Reduction - Provides optimized model structures that reduce memory usage and computation time to lower inference latency.
- Inference Speed Optimizers - Optimizes the execution speed and memory usage of neural network inference for faster live predictions.
- Vision Transformer Implementations - Implements Vision Transformer architectures optimized for reduced parameter counts and high inference efficiency.
- Efficient Neural Architectures - Offers a set of lightweight neural network backbones designed to reduce computational and memory overhead.
- Efficient Vision Transformers - Implements efficient vision transformers with optimized attention mechanisms to reduce resource requirements.
- Mobile AI Architectures - Develops AI backbones optimized for the strict energy and memory limits of mobile device hardware.
- MLP-Mixer Architectures - Utilizes MLP-Mixer architectures to process visual data using multi-layer perceptrons for high-performance feature extraction.
- Neural Network Design Frameworks - Provides a collection of optimized layers and structural design patterns to minimize parameter counts in neural networks.
- Weight Tying Strategies - Implements tied-weight transformer blocks that share parameters across layers to decrease total model size and memory footprint.
- Neural Network Architectures - Lightweight architecture using cheap operations for feature generation.
Star history
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