Yutong Bai (ytongbai)

ytongbai

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Company:Johns Hopkins University

Home Page:yutongbai.com

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Yutong Bai's starred repositories

llama

Inference code for LLaMA models

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CLIP

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

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pytorch_geometric

Graph Neural Network Library for PyTorch

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Awesome-pytorch-list

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

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simple-faster-rcnn-pytorch

A simplified implemention of Faster R-CNN that replicate performance from origin paper

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EasyLM

Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.

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cmr

Project repo for Learning Category-Specific Mesh Reconstruction from Image Collections

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MGMatting

This repository includes the official project of Mask Guided (MG) Matting, presented in our paper: Mask Guided Matting via Progressive Refinement Network

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DeepLabv3.pytorch

PyTorch implementation of DeepLabv3

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tf-lift

Tensorflow port of LIFT (ECCV 2016), with training code.

RobustCNN

[ICLR 2023] This repository includes the official implementation our paper "Can CNNs Be More Robust Than Transformers?"

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CompositionalNets

Official implementation of CVPR2020 paper: "Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion" https://arxiv.org/abs/2003.04490

OrganSegRSTN_PyTorch

PyTorch implementation of OrganSegRSTN - CVPR 2018

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video-clip-order-prediction

Self-supervised Spatiotemporal Learning via Video Clip Order Prediction

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mlxu

Machine Learning eXperiment Utilities

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VoGE

VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis [ICLR 2023]

CAKES

This repository contains the code for our AAAI2021 paper CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks.

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SemanticPartDetection

Pytorch implementation for the ICCV2019: Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints from Limited Training Data