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Training library for local feature detection and matching

Research on Tabular Deep Learning: Papers & Packages

Samples for Intel® oneAPI Toolkits

[NeurIPS 2020] Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Includes a PyTorch library for deep learning with SVG data.

Cool vision, learning, and graphics papers on Cats!

[ICLR24] Official implementation of the paper “MagicDrive: Street View Generation with Diverse 3D Geometry Control”

JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.

:books: A collection of Deep Learning based Image Colorization and Video Colorization papers.

TensorFlow 最佳学习资源大全（含课程、书籍、博客、公开课等内容）

A self-learning tutorail for CUDA High Performance Programing.

Pytorch Implementation For LPRNet, A High Performance And Lightweight License Plate Recognition Framework.

Tools for handling multimodal data in machine learning projects.

OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.

Machine Learning Yearning book by 🅰️𝓷𝓭𝓻𝓮𝔀 🆖

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.

FAIR Sequence Modeling Toolkit 2

Tez is a super-simple and lightweight Trainer for PyTorch. It also comes with many utils that you can use to tackle over 90% of deep learning projects in PyTorch.

Plug and Play Language Model implementation. Allows to steer topic and attributes of GPT-2 models.

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

Demo programs for the Talking Head Anime from a Single Image 2: More Expressive project.

机器学习/深度学习/Python/Go语言面试题笔试题(Machine learning Deep Learning Python and Golang Interview Questions)

🔥 CNN for Watermark Removal using Deep Image Prior with Pytorch 🔥.

机器学习&深度学习网站资源汇总（Machine Learning Resources）

🏖 Keras Implementation of Painting outside the box

The nnsight package enables interpreting and manipulating the internals of deep learned models.

An autoML framework & toolkit for machine learning on graphs.

🔥 An autonomous AI agent that runs your deep learning experiments 24/7 while you sleep. Zero-cost monitoring, Leader-Worker architecture, constant-size memory.

Human Pose estimation with TensorFlow framework

ChirpStack open-source LoRaWAN Network Server

Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA

Neural question generation using transformers

An application tool of edge-connect, which can do anime inpainting and drawing. 动漫人物图片自动修复，去马赛克，填补，去瑕疵

:computer: Learn to make machines learn so that you don't have to struggle to program them; The ultimate list

Audio processing by using pytorch 1D convolution network

A List of Data Science/Machine Learning Resources (Mostly Free)

Video classification tools using 3D ResNet

[CVPR 2024 Highlight] Putting the Object Back Into Video Object Segmentation

Change Python code while it's running without losing state

A lightweight header-only library for using Keras (TensorFlow) models in C++.

Model Zoo For OpenCV DNN and Benchmarks.

Samples and Tools for Windows ML.

DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models.   🤖💤

UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery, ISPRS. Also, including other vision transformers and CNNs for satellite, aerial image and UAV image segmentation.

SwissArmyTransformer is a flexible and powerful library to develop your own Transformer variants.

PyCIL: A Python Toolbox for Class-Incremental Learning

Notes for Fastai Deep Learning Course

A PyTorch toolkit for 2D Human Pose Estimation.

EfficientFormerV2 [ICCV 2023] & EfficientFormer [NeurIPs 2022]

My implementation of the original transformer model (Vaswani et al.). I've additionally included the playground.py file for visualizing otherwise seemingly hard concepts. Currently included IWSLT pretrained models.