A latent text-to-image diffusion model

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

The fastai book, published as Jupyter Notebooks

Learn OpenCV : C++ and Python Examples

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

A collection of various deep learning architectures, models, and tips

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

High-Resolution Image Synthesis with Latent Diffusion Models

强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/

🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022

Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion

Using Low-rank adaptation to quickly fine-tune diffusion models.

Official Code for Stable Cascade

数据挖掘、计算机视觉、自然语言处理、推荐系统竞赛知识、代码、思路

Material for gpu-mode lectures

PyTorch入门教程，在线阅读地址：https://datawhalechina.github.io/thorough-pytorch/

机器学习初学者公众号作品

Awesome-pytorch-list 翻译工作进行中......

OneDiff: An out-of-the-box acceleration library for diffusion models.

MindSpore online courses: Step into LLM

天池医疗AI大赛[第一季]：肺部结节智能诊断 UNet/VGG/Inception/ResNet/DenseNet

这是一个完整的，端到端的机器学习项目，非常适合有一定基础后拿来练习，以提高对完整机器学习项目的认识

主要存储Datawhale组队学习中“计算机视觉”方向的资料。

A curated list of Machine Learning and Deep Learning tutorials in Jupyter Notebook format ready to run in Google Colaboratory

Kaggle airbus ship detection challenge 21st solution

Local Texture Estimator for Implicit Representation Function, in CVPR 2022

Regularizing Generative Adversarial Networks under Limited Data (CVPR 2021)

Accepted by New Trends in Image Restoration and Enhancement workshop (NTIRE), in conjunction with CVPR 2024.