Awesome GitHub RepositoriesImage Data Preprocessing

LivePortrait is a deep learning framework for portrait animation that transfers facial expressions from a driving video to a static image. It functions as an AI motion retargeting tool, mapping movements between different identities while preserving the unique features of the source portrait. The system includes specialized capabilities for cross-species portrait animation, adapting human-centric models to non-human subjects and animals. It also features a motion template generator that converts driving videos into portable files to accelerate inference and protect the identity of the origina

FaceNet is a facial recognition framework designed to transform facial images into high-dimensional numerical embeddings for identity verification and recognition. It provides a deep learning face embedder that maps facial features into a Euclidean space where distance corresponds to facial similarity. The system includes tools for both supervised and unsupervised identity management. It features a face identity classifier for categorizing images into known identity classes and an unsupervised clustering tool to group similar facial embeddings together without predefined labels. The framewor

This project is a collection of supervised and unsupervised machine learning algorithms implemented from scratch using Python. It serves as an educational resource for studying model training, parameter optimization, and the implementation of core predictive models. The library provides a variety of supervised learning tools, including linear and logistic regression, decision trees, and support vector machines. It also features unsupervised learning capabilities for discovering patterns in unlabeled datasets through clustering algorithms. Broad capability areas include ensemble learning thro

MMSegmentation is an open-source semantic segmentation toolbox built on PyTorch that provides a modular, configurable framework for building, training, evaluating, and deploying segmentation models. At its core, it offers a config-driven pipeline that assembles training, evaluation, and inference workflows by parsing hierarchical configuration files, with a modular component registry that enables plug-and-play composition of neural network modules, optimizers, datasets, and metrics. The framework supports the full model lifecycle through a unified runner interface that controls training, testi

NSFW detection on the client-side via TensorFlow.js

mmagic is a multimodal training pipeline and framework for generative AI, focusing on visual synthesis and restoration. It provides the infrastructure to build and train models for tasks such as text-to-image and text-to-video generation, 3D-aware content synthesis, and high-fidelity image translation using diffusion models and generative adversarial networks. The project distinguishes itself through specialized capabilities for generative model personalization, including techniques for fine-tuning subjects and styles. It also supports advanced visual manipulations such as latent space interp

Anti-Anti-Spider is an automated web scraping toolkit and CAPTCHA bypass framework. It uses convolutional neural networks to recognize characters and digits in image-based security challenges, enabling programmatic access to protected web content. The project functions as an image recognition model trainer, providing a workflow to preprocess labeled image datasets and train custom neural networks. Users can configure model architectures and hyperparameters to align the recognition system with the visual style of specific target websites. The toolkit covers capabilities for image data preproc

This project is a machine learning educational resource and implementation guide for Python. It provides a collection of executable code and notebooks that demonstrate predictive modeling, data analysis workflows, and the implementation of various machine learning algorithms. The repository features practical examples of classification, regression, and clustering tasks using Scikit-Learn, alongside tutorials for building and training deep learning architectures with TensorFlow. These include implementations of convolutional and recurrent networks. The content covers a broad range of capabili

mmcv is a foundation library for computer vision based on PyTorch. It provides a comprehensive system for constructing convolutional neural networks, a toolkit for image and video preprocessing, and a collection of high-performance deep learning vision operators. The project is distinguished by its hardware-accelerated kernels for complex operations such as deformable convolutions and region pooling. It features a configuration-driven framework that allows for the dynamic instantiation of network layers and the registration of custom modules without modifying code. The library covers a broad

LatentSync 是一个音频驱动的视频生成器和潜在扩散唇形同步模型，旨在将视频中说话者的唇形动作与目标音轨同步。它提供了一个唇形同步训练框架，用于在自定义视频和音频数据集上开发同步网络。 该系统利用视频预处理流水线来清理、分割和对齐人脸数据。它包括一个视觉同步评估工具，该工具计算置信度分数以衡量生成视频中音频和视觉对齐的准确性。 该项目涵盖了自定义同步网络开发、针对硬件内存和分辨率的训练配置管理以及合成视频评估的功能。

本项目是一系列 PyTorch 深度学习课程，包含实践项目和编程练习。它专注于实现神经网络架构和模型训练，以解决复杂的数据问题。 该仓库包含一个计算机视觉项目套件，用于构建图像分类器、自动编码器和风格迁移应用。它具有用于创建合成图像的生成对抗网络（GAN）实验室，以及用于将预训练权重适配到新任务的迁移学习实现。 代码库涵盖了使用循环神经网络和词嵌入进行自然语言处理的序列数据分析。其他功能包括图像数据预处理、模型性能评估以及将训练好的模型部署到云基础设施。 这些材料以一系列 Jupyter Notebook 的形式提供。

This project is a comprehensive machine learning educational resource and tutorial series delivered as a collection of interactive Jupyter Notebooks. It provides practical Python implementations for the end-to-end machine learning lifecycle, covering supervised and unsupervised learning, deep learning, and reinforcement learning. The resource distinguishes itself by providing detailed implementation guides for complex architectures, including transformers, generative adversarial networks, and convolutional neural networks. It also features specialized courseware for developing reinforcement l

Augmentor 是一个 Python 图像增强库和框架，旨在扩展机器学习数据集。它既是一个生成合成图像变体以增加数据多样性的预处理工具，也是一个训练数据流处理器，可将增强后的图像和标签直接馈送到神经网络循环中，而无需中间磁盘存储。 该框架保持图像与其对应掩码之间的空间对齐，这是语义分割训练所必需的。它支持多种几何和像素级变换，包括弹性形变、通过倾斜和扭曲进行的透视变换、旋转、剪切以及随机区域擦除。 该系统包含针对类别处理的策略以解决数据不平衡问题，并使用多线程来加速增强数据集的并行生成。它还提供了在预处理阶段清理和标准化原始图像文件的实用程序。

This is an image segmentation framework and masking toolkit for constructing binary and multi-class neural network architectures. It serves as a deep learning encoder wrapper that integrates pre-trained convolutional neural network architectures into semantic segmentation models. The library enables the use of pre-trained backbones to isolate complex patterns and leverages transfer learning to accelerate training. It provides a collection of overlap-based loss functions and precision metrics specifically designed to evaluate and refine the accuracy of image masks. The toolkit covers the full

mmocr 是一个基于 PyTorch 的光学字符识别（OCR）框架，旨在训练和部署文本检测、识别和关键信息提取模型。它作为一个全面的场景文本检测和识别工具箱，提供用于定位文本区域并将视觉文本转换为机器编码字符串的专用库。 该项目的独特之处在于用于关键信息提取的研究框架和高级文本定位功能。这些包括使用 Transformer 的基于点的定位，以及使用参数化贝塞尔曲线来识别和转录任意形状的文本。 该框架涵盖了广泛的计算机视觉功能，包括用于增强和标准化多样化 OCR 数据集的流水线管理、具有分布式扩展的模型训练，以及使用标准 OCR 指标的性能评估。它还提供用于几何多边形操作和结果可视化的实用程序，以便根据真实标注审计预测。 该系统使用 Python 实现，并支持通过 Docker 环境打包进行安装。

这是一个关于使用 PyTorch 构建神经网络的综合教学资源和课程。它涵盖了深度学习的基本构建块，包括张量操作、自动微分以及模块化神经网络组件的构建。 该仓库是多个专业领域的参考指南。它提供了计算机视觉任务（如图像分类、目标检测和语义分割）的实现细节，以及涉及 Transformer、循环网络和生成模型的自然语言处理工作流。此外，它还包括生成式 AI 的参考资料，专门关注通过扩散模型和对抗网络进行图像合成。 材料延伸至模型优化和部署流水线。它涵盖了通过量化和将模型导出为 ONNX 和 TensorRT 等格式来减小模型大小并提高推理速度的技术。其他能力领域包括用于并行加载的数据工程、使用自定义指标的模型评估，以及开源大语言模型的部署。 该项目主要以一系列 Jupyter Notebook 的形式提供。

img2dataset 是一个高性能图像数据集流水线和预处理工具，旨在为机器学习训练从 URL 下载并处理数百万张图像。它作为一个分布式图像下载器和云存储数据导出器，将大型视觉数据集从 Web 源直接移动到结构化格式中。 该系统通过在多个 CPU 核心和机器之间分配工作负载，优先考虑高吞吐量的数据获取。它直接与远程云存储桶集成，并采用基于清单的追踪系统，无需重新处理现有数据即可恢复中断的下载。 该工具为机器学习数据集准备提供了完整的预处理套件，包括图像缩放、裁剪以及基于尺寸或长宽比的属性过滤。它还通过哈希比较验证图像完整性，并在抓取工作流期间确保符合机器人协议。 该项目使用 Python 实现。

该项目是一个使用 Python 从零实现的机器学习算法与工具集合。它作为一个核心算法库，涵盖了回归、分类和聚类模型，旨在展示这些算法底层的数学结构，而不依赖于高层机器学习框架。 该项目专注于算法逻辑的手动实现，包括带有前向传播和权重更新的神经网络，以及多种监督和无监督学习模型。它利用 NumPy 进行向量化处理，以对大规模数据集执行矩阵计算和数学运算。 该工具包涵盖了广泛的功能，包括通过主成分分析（PCA）进行降维，以及针对数值和图像数据集的数据预处理。算法实现涵盖了线性回归、贝叶斯回归、K-Means 聚类，以及支持向量机（SVM）、决策树和 K-近邻（KNN）等多种分类方法。 该项目以一系列 Jupyter Notebook 的形式提供。

sam-hq 是一系列预训练视觉基础模型和适配器，专为高质量图像分割、多模态特征提取和深度估计而设计。它提供了一个零样本（zero-shot）视觉模型，能够在不同领域执行分割和分类，而无需特定任务的训练。 该项目具有基于 Segment Anything Model 的高质量图像分割工具，可从空间提示生成精确掩码。它包括一个多模态特征提取器，用于从图像和文本输入生成高维向量嵌入，以及一个用于从视觉数据预测距离或冠层高度的卷积工具。 该框架涵盖了广泛的计算机视觉功能，包括图像分类、多分辨率特征提取和图像预处理。它支持通过在自定义数据集上进行微调来实现领域自适应，适用于医学影像和遥感等专业应用。 掩码解码器可以转换为开放格式，以便在具有标准运行时的环境中执行。

ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It provides an ahead-of-time compilation pipeline that exports, quantizes, and lowers model graphs into compact serialized programs, then executes them through a minimal runtime with hardware acceleration and on-device large language model inference capabilities. The project distinguishes itself through a hardware accelerator delegate system that partitions model subgraphs and offloads computation to specialized backends including NPUs, GPUs, and DSPs from Apple, Arm, Intel, MediaTek,