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This is a PyTorch implementation of EfficientNet convolutional neural networks. It serves as a computer vision model library providing architectures for image classification and high-level feature extraction, including pre-trained weights for immediate image categorization. The library supports transfer learning by allowing the modification of model architectures and output layers to accommodate a custom number of classes for new datasets. It also includes a model exporter to convert trained PyTorch weights into the ONNX format for production inference. The system covers broader computer vis

This repository provides a collection of reference implementations, toolkits, and orchestration tools for training and deploying large-scale AI models on Cloud TPU hardware. It serves as a framework for managing the lifecycle of accelerator clusters, including hardware orchestration and the provisioning of high-performance compute infrastructure for machine learning workloads. The project specifically enables the pre-training of foundation models, large language models, and complex reasoning architectures through distributed training toolkits and multi-host scaling recipes. It further provide

by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page.

By Songtao Liu, Di Huang, Yunhong Wang

This is an unofficial PyTorch implementation of DeepLab v2 1 with a ResNet-101 backbone. COCO-Stuff dataset 2 and PASCAL VOC dataset 3 are supported. The official Caffe weights provided by the authors can be used without building the Caffe APIs. DeepLab v3/v3+ models with the identical…

Update (Aug 17, 2021): refactored the code of ACB. The readability has been greatly improved. You may call switchtodeploy of an ACB to convert it to the inference-time structure. If you use ACB in your own model, the conversion is as easy as `` for m in yourmodel.modules(): if hasattr(m,…

This repository includes the code for training and evaluating the SSH face detector introduced in our ICCV 2017 paper.

This repository is a PyTorch implementation for our CVPR2020 paper (non-commercial use only).

This is the PyTorch implementation of our paper "Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition". (Note that this is the code for image recognition on ImageNet. For semantic image segmentation/parsing refer to this repository:…

Created by Jinming Cao , Yangyan Li , Mingchao Sun, Ying Chen , Dani Lischinski , Daniel Cohen-Or , Baoquan Chen , and Changhe Tu . Transactions on Image Processing (TIP) 2022.

ECCV 2020 PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer

This repository contains the code (in PyTorch) for SlimConv: Reducing Channel Redundancy in Convolutional Neural Networks by Features Recombining paper (TIP 2021)

A new method to fuse high-level features and low-level features in human pose estimation