Reference models and tools for Cloud TPUs.

Last update: Jan 05, 2023

Related tags

Overview

Cloud TPUs

This repository is a collection of reference models and tools used with Cloud TPUs.

The fastest way to get started training a model on a Cloud TPU is by following the tutorial. Click the button below to launch the tutorial using Google Cloud Shell.

Note: This repository is a public mirror, pull requests will not be accepted. Please file an issue if you have a feature or bug request.

Running Models

To run models in the models subdirectory, you may need to add the top-level /models folder to the Python path with the command:

export PYTHONPATH="$PYTHONPATH:/path/to/models"

Owner

GitHub Repository https://cloud.google.com/tpu/

Bald-to-Hairy Translation Using CycleGAN

GANiry: Bald-to-Hairy Translation Using CycleGAN Official PyTorch implementation of GANiry. GANiry: Bald-to-Hairy Translation Using CycleGAN, Fidan Sa

10 Oct 27, 2022

Pytorch ImageNet1k Loader with Bounding Boxes.

ImageNet 1K Bounding Boxes For some experiments, you might wanna pass only the background of imagenet images vs passing only the foreground. Here, I'v

11 Oct 15, 2022

The official implementation of ELSA: Enhanced Local Self-Attention for Vision Transformer

ELSA: Enhanced Local Self-Attention for Vision Transformer By Jingkai Zhou, Pich

87 Dec 19, 2022

Bag of Tricks for Natural Policy Gradient Reinforcement Learning

Bag of Tricks for Natural Policy Gradient Reinforcement Learning [ArXiv] Setup Python 3.8.0 pip install -r req.txt Mujoco 200 license Main Files main.

1 Oct 10, 2022

In this project, RandomOverSampler and SMOTE algorithms were used to perform oversampling, ClusterCentroids algorithm was used to undersampling, SMOTEENN algorithm was applied as a combinatorial approach of over- and undersampling of credit card credit dataset from LendingClub. Machine learning models - BalancedRandomForestClassifier and EasyEnsembleClassifier were used to predict credit risk.

Overview of Credit Card Analysis In this project, RandomOverSampler and SMOTE algorithms were used to perform oversampling, ClusterCentroids algorithm

1 Mar 12, 2022

Unofficial PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution

PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution [arXiv 2021].

122 Dec 12, 2022

Learned Initializations for Optimizing Coordinate-Based Neural Representations

Learned Initializations for Optimizing Coordinate-Based Neural Representations Project Page | Paper Matthew Tancik*1, Ben Mildenhall*1, Terrance Wang1

127 Jan 03, 2023

Deep Latent Force Models

Deep Latent Force Models This repository contains a PyTorch implementation of the deep latent force model (DLFM), presented in the paper, Compositiona

5 Oct 26, 2022

A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron weights are available.

Use this instead: https://github.com/facebookresearch/maskrcnn-benchmark A Pytorch Implementation of Detectron Example output of e2e_mask_rcnn-R-101-F

2.8k Dec 29, 2022

Resources for the Ki testnet challenge

Ki Testnet Challenge This repository hosts ki-testnet-challenge. A set of scripts and resources to be used for the Ki Testnet Challenge What is the te

23 Aug 08, 2022

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving (AAAI2021). RTS3D is efficiency and accuracy s

71 Nov 29, 2022

Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection

An official implementation of paper Data-Uncertainty Guided Multi-Phase Learning for Semi-supervised Object Detection

11 Nov 23, 2022

Codebase for the paper titled "Continual learning with local module selection"

This repository contains the codebase for the paper Continual Learning via Local Module Composition. Setting up the environemnt Create a new conda env

20 Dec 10, 2022

NFT-Price-Prediction-CNN - Using visual feature extraction, prices of NFTs are predicted via CNN (Alexnet and Resnet) architectures.

5 Nov 03, 2022