hieptran1812

YOLOv8_Efficient

🚀Simple and efficient use for Ultralytics yolov8🚀

training-operator

Distributed ML Training and Fine-Tuning on Kubernetes

awesome-kubeflow

A curated list of awesome projects and resources related to Kubeflow (a CNCF incubating project)

CasMVSNet_pl

Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching using pytorch-lightning

cupy

NumPy & SciPy for GPU

tabml

Awesome-GPU

Awesome resources for GPUs

mae

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

C-3-Framework

An open-source PyTorch code for crowd counting

Next-ViT

paper-reading

深度学习经典、新论文逐段精读

pytorch-metric-learning

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

arcface-pytorch

phenaki-pytorch

Implementation of Phenaki Video, which uses Mask GIT to produce text guided videos of up to 2 minutes in length, in Pytorch

Awesome-Crowd-Counting

Awesome Crowd Counting

6DRepNet

Official Pytorch implementation of 6DRepNet: 6D Rotation representation for unconstrained head pose estimation.

nanoGPT

The simplest, fastest repository for training/finetuning medium-sized GPTs.

PIPNet

Efficient facial landmark detector

ConvNeXt-V2

Code release for ConvNeXt V2 model

optuna

A hyperparameter optimization framework

MONAI

AI Toolkit for Healthcare Imaging

pretrained-models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

youtube-8m

Starter code for working with the YouTube-8M dataset.

writer

✍️ AI powered documentation writer

PMG-Progressive-Multi-Granularity-Training

Code release for Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches (ECCV2020)

mmpose

OpenMMLab Pose Estimation Toolbox and Benchmark.

imgclsmob

Sandbox for training deep learning networks

vision

Datasets, Transforms and Models specific to Computer Vision

point-e

Point cloud diffusion for 3D model synthesis

lime

Lime: Explaining the predictions of any machine learning classifier