Reference models and tools for Cloud TPUs.

Last update: Jan 05, 2023

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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/

Person Re-identification

Person Re-identification Final project of Computer Vision Table of content Person Re-identification Table of content Students: Proposed method Dataset

4 Jun 17, 2021

Source code for our EMNLP'21 paper 《Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning》

Child-Tuning Source code for EMNLP 2021 Long paper: Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning. 1. Environ

46 Dec 12, 2022

This repository is for our paper Exploiting Scene Graphs for Human-Object Interaction Detection accepted by ICCV 2021.

SG2HOI This repository is for our paper Exploiting Scene Graphs for Human-Object Interaction Detection accepted by ICCV 2021. Installation Pytorch 1.7

10 Dec 20, 2022

Morphable Detector for Object Detection on Demand

Morphable Detector for Object Detection on Demand (ICCV 2021) PyTorch implementation of the paper Morphable Detector for Object Detection on Demand. I

9 Feb 23, 2022

Scalable Optical Flow-based Image Montaging and Alignment

SOFIMA SOFIMA (Scalable Optical Flow-based Image Montaging and Alignment) is a tool for stitching, aligning and warping large 2d, 3d and 4d microscopy

16 Dec 21, 2022

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

ENet in Caffe Execution times and hardware requirements Network 1024x512 1280x720 Parameters Model size (fp32) ENet 20.4 ms 32.9 ms 0.36 M 1.5 MB SegN

561 Jan 04, 2023

Lightweight Python library for adding real-time object tracking to any detector.

Norfair is a customizable lightweight Python library for real-time 2D object tracking. Using Norfair, you can add tracking capabilities to any detecto

1.7k Jan 05, 2023

The first dataset on shadow generation for the foreground object in real-world scenes.

Object-Shadow-Generation-Dataset-DESOBA Object Shadow Generation is to deal with the shadow inconsistency between the foreground object and the backgr

105 Dec 30, 2022

Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL)

LUPerson-NL Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL) The repository is for our CVPR2022 paper Large-Scale

43 Dec 26, 2022

Small-bets - Ergodic Experiment With Python

Ergodic Experiment Based on this video. Run this experiment with this command: p

3 Jan 11, 2022

Using some basic methods to show linkages and transformations of robotic arms

roboticArmVisualizer Python GUI application to create custom linkages and adjust joint angles. In the future, I plan to add 2d inverse kinematics solv

1 Nov 19, 2021

Code for paper "Multi-level Disentanglement Graph Neural Network"

Multi-level Disentanglement Graph Neural Network (MD-GNN) This is a PyTorch implementation of the MD-GNN, and the code includes the following modules:

6 Dec 29, 2022

Code for paper: "Spinning Language Models for Propaganda-As-A-Service"

Spinning Language Models for Propaganda-As-A-Service This is the source code for the Arxiv version of the paper. You can use this Google Colab to expl

16 Jan 03, 2023

Implementation for paper "Towards the Generalization of Contrastive Self-Supervised Learning"

Contrastive Self-Supervised Learning on CIFAR-10 Paper "Towards the Generalization of Contrastive Self-Supervised Learning", Weiran Huang, Mingyang Yi

13 Nov 30, 2022

Official implementation for the paper: Multi-label Classification with Partial Annotations using Class-aware Selective Loss

Multi-label Classification with Partial Annotations using Class-aware Selective Loss Paper | Pretrained models Official PyTorch Implementation Emanuel

99 Dec 27, 2022