Efficient-AI-Backbones
deepvision
Efficient-AI-Backbones | deepvision | |
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3 | 1 | |
3,816 | 30 | |
1.5% | - | |
5.8 | 5.1 | |
6 days ago | 10 months ago | |
Python | Python | |
- | Apache License 2.0 |
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Efficient-AI-Backbones
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Researchers From China Introduce Vision GNN (ViG): A Graph Neural Network For Computer Vision Systems
Continue reading | Check out the paper, github
- GNN for computer vision, beating CNN & Transformer
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GNN can also work well on computer vision
Vision GNN: An Image is Worth Graph of Nodes Network architecture plays a key role in the deep learning-based computer vision system. The widely-used convolutional neural network and transformer treat the image as a grid or sequence structure, which is not flexible to capture irregular and complex objects. In this paper, we propose to represent the image as a graph structure and introduce a new Vision GNN (ViG) architecture to extract graph-level feature for visual tasks. We first split the image to a number of patches which are viewed as nodes, and construct a graph by connecting the nearest neighbors. Based on the graph representation of images, we build our ViG model to transform and exchange information among all the nodes. ViG consists of two basic modules: Grapher module with graph convolution for aggregating and updating graph information, and FFN module with two linear layers for node feature transformation. Both isotropic and pyramid architectures of ViG are built with different model sizes. Extensive experiments on image recognition and object detection tasks demonstrate the superiority of our ViG architecture. We hope this pioneering study of GNN on general visual tasks will provide useful inspiration and experience for future research. The PyTroch code will be available at https://github.com/huawei-noah/CV-Backbones.
deepvision
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PyTorch/TensorFlow image models with automatic weight conversions
Link: https://github.com/DavidLandup0/deepvision
What are some alternatives?
MPViT - [CVPR 2022] MPViT:Multi-Path Vision Transformer for Dense Prediction
pytorch2keras - PyTorch to Keras model convertor
FQ-ViT - [IJCAI 2022] FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
awesome-modular-pytorch-lightning - LightCollections⚡️: Ready-to-use implementations such as `LightningModules` for various computer vision papers.
transfiner - Mask Transfiner for High-Quality Instance Segmentation, CVPR 2022
SHREC2023-ANIMAR - Source codes of team TikTorch (1st place solution) for track 2 and 3 of the SHREC2023 Challenge
RethinkVSRAlignment - (NIPS 2022) Rethinking Alignment in Video Super-Resolution Transformers
Open3D-ML - An extension of Open3D to address 3D Machine Learning tasks
PyTorch-Vision-Transformer-ViT-MNIST-CIFAR10 - Simplified Pytorch implementation of Vision Transformer (ViT) for small datasets like MNIST, FashionMNIST, SVHN and CIFAR10.
datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
Pretrained-Language-Model - Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.
dream-creator - Quickly and easily create / train a custom DeepDream model