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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 i
mmpretrain is a modular PyTorch computer vision framework designed for developing, training, and benchmarking deep learning architectures. It serves as a comprehensive toolkit for vision tasks, providing a specialized platform for multimodal machine learning and self-supervised learning. The project features a computer vision model zoo containing architectural definitions and pre-trained weights for backbones such as ViT, ConvNeXt, and Swin Transformer. It distinguishes itself through a dedicated self-supervised learning toolkit that implements algorithms like MAE and DINO to train models wit
DeiT is a PyTorch vision transformer framework designed for image classification. It implements a transformer-based architecture that processes images as sequences of flattened patches using self-attention layers and position-aware sequence modeling instead of convolutional filters. The project focuses on data-efficient training through a knowledge distillation framework. This system allows a student model to mimic the soft labels of a high-performance teacher model to improve accuracy and generalization, particularly when training on smaller datasets. The library covers the full development
Corenet is a deep learning training framework and computer vision model library designed for developing neural networks across vision, text, and audio modalities. It functions as a distributed training orchestrator for scaling workloads across multiple compute nodes and provides a multimodal data pipeline for processing image, text, and video data. The project includes a model conversion toolkit for transforming weights and architectures between different machine learning frameworks. It also provides tools for optimizing model performance on Apple Silicon and reducing response latency in gene
Efficient-AI-Backbones is a lightweight neural network library and computer vision model zoo. It provides a collection of optimized deep learning backbones designed to minimize computational overhead and memory usage for artificial intelligence tasks.
The main features of huawei-noah/efficient-ai-backbones are: Computer Vision Models, Lightweight Architectures, Edge Hardware Optimizations, Memory-Efficient Deep Learning, Modular Backbone Architectures, Lightweight Model Implementations, Efficient Neural Architectures, Gated Activation Computations.
Open-source alternatives to huawei-noah/efficient-ai-backbones include: huawei-noah/ghostnet — GhostNet provides a set of efficient AI model architectures and neural network design patterns designed to reduce… open-mmlab/mmpretrain — mmpretrain is a modular PyTorch computer vision framework designed for developing, training, and benchmarking deep… facebookresearch/deit — DeiT is a PyTorch vision transformer framework designed for image classification. It implements a transformer-based… apple/corenet — Corenet is a deep learning training framework and computer vision model library designed for developing neural… huggingface/pytorch-image-models — This project is a comprehensive library of state-of-the-art neural network architectures designed for image… rwightman/pytorch-image-models — This project is a library of pretrained computer vision architectures and backbones for image classification and…