[ECCV 2020] PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer
d-li14/psconv 的主要功能包括：Neural Network Architectures, Plug and Play Modules。
d-li14/psconv 的开源替代品包括： tensorflow/tpu — This repository provides a collection of reference implementations, toolkits, and orchestration tools for training and… lukemelas/efficientnet-pytorch — This is a PyTorch implementation of EfficientNet convolutional neural networks. It serves as a computer vision model… blealtan/efficient-kan — This project is a PyTorch library for building and training Kolmogorov-Arnold Networks. It implements a neural network… pageman/sutskever-30-implementations — This project is a collection of deep learning research implementations and a reproduction kit designed to translate… microsoft/ai-edu — ai-edu is a comprehensive AI education curriculum and machine learning courseware collection. It provides theoretical… facebookresearch/slowfast — SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset…
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
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 project is a PyTorch library for building and training Kolmogorov-Arnold Networks. It implements a neural network architecture that replaces fixed activation functions with learnable spline-based functions on edges, serving as a tool for interpretable machine learning. The implementation utilizes reformulated matrix operations to reduce memory overhead and increase computation speed. It employs L1 regularization to sparsify network weights, which improves the transparency of the model's internal logic and decisions. The framework covers a range of capabilities including grid-based funct
SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset for video action recognition, enabling the training and evaluation of models designed to classify complex activities and objects within video sequences. The framework is distinguished by its use of dual-pathway spatiotemporal sampling to capture both slow and fast motions. It supports self-supervised video learning for pre-training models on unlabeled data and employs multigrid spatiotemporal training to optimize learning across multiple spatial and temporal resolutions. The