huawei-noah/Efficient-AI-Backbones

• huawei-noah/ghostnet

  huawei-noah/ghostnet

  4,416View on GitHub↗

  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

  Python

  View on GitHub↗4,416
• facebookresearch/deit

  facebookresearch/deit

  4,348View on GitHub↗

  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

  Python

  View on GitHub↗4,348
• apple/corenet

  apple/corenet

  6,999View on GitHub↗

  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

  Jupyter Notebook

  View on GitHub↗6,999
• huggingface/pytorch-image-models

  huggingface/pytorch-image-models

  36,893View on GitHub↗

  This project is a comprehensive library of state-of-the-art neural network architectures designed for image classification and feature extraction. It provides a complete deep learning training framework that supports distributed execution, allowing users to build, train, and fine-tune vision models using optimized schedulers and pre-configured training recipes. The library distinguishes itself through a modular backbone architecture that treats neural networks as decoupled feature extractors, enabling the retrieval of multi-scale outputs for downstream tasks like object detection and segmenta

  Pythonaugmixconvnextdistributed-training

  View on GitHub↗36,893

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