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Open Source Python Machine Learning Software for Windows

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Python Machine Learning Software for Windows

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  • 1
    AI-Aimbot

    AI-Aimbot

    CS2, Valorant, Fortnite, APEX, every game

    AI-Aimbot is a computer vision project that demonstrates how artificial intelligence can be used to automatically identify and target opponents in video games. The system uses an object detection model based on the YOLOv5 architecture to detect human-shaped characters in gameplay screenshots or video frames. Once a target is identified, the program automatically adjusts the player’s aim toward the detected target, effectively automating the aiming process in first-person shooter games. The project emphasizes that it is intended for educational purposes to illustrate potential vulnerabilities in game design and anti-cheat systems. Because the system relies solely on visual detection rather than reading game memory, it attempts to bypass certain traditional anti-cheat detection methods.
    Downloads: 7,972 This Week
    Last Update:
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  • 2
    DeepFaceLive

    DeepFaceLive

    Real-time face swap for PC streaming or video calls

    You can swap your face from a webcam or the face in the video using trained face models. There is also a Face Animator module in DeepFaceLive app. You can control a static face picture using video or your own face from the camera. The quality is not the best, and requires fine face matching and tuning parameters for every face pair, but enough for funny videos and memes or real-time streaming at 25 fps using 35 TFLOPS GPU.
    1 Review
    Downloads: 373 This Week
    Last Update:
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  • 3
    X-AnyLabeling

    X-AnyLabeling

    Effortless data labeling with AI support from Segment Anything

    X-AnyLabeling is an open-source data annotation platform designed to streamline the process of labeling datasets for computer vision and multimodal AI applications. The software integrates an AI-powered labeling engine that allows users to generate annotations automatically with the assistance of modern vision models such as Segment Anything and various object detection frameworks. It supports labeling tasks across images and videos and enables developers to prepare training datasets for tasks such as object detection, segmentation, classification, tracking, and pose estimation. The tool is built with an interactive graphical interface that simplifies annotation workflows and allows users to draw and edit labels directly on visual data. It also supports a wide range of export formats compatible with popular machine learning pipelines, making it easier to integrate with training frameworks.
    Downloads: 117 This Week
    Last Update:
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  • 4
    DeepMosaics

    DeepMosaics

    Automatically remove the mosaics in images and videos, or add mosaics

    Automatically remove the mosaics in images and videos, or add mosaics to them. This project is based on "semantic segmentation" and "Image-to-Image Translation". You can either run DeepMosaics via a pre-built binary package, or from source. Run time depends on the computer's performance (GPU version has better performance but requires CUDA to be installed). Different pre-trained models are suitable for different effects.[Introduction to pre-trained models].
    Downloads: 69 This Week
    Last Update:
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  • 5
    GFPGAN

    GFPGAN

    GFPGAN aims at developing Practical Algorithms

    GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration. Colab Demo for GFPGAN; (Another Colab Demo for the original paper model) Online demo: Huggingface (return only the cropped face) Online demo: Replicate.ai (may need to sign in, return the whole image). Online demo: Baseten.co (backed by GPU, returns the whole image). We provide a clean version of GFPGAN, which can run without CUDA extensions. So that it can run in Windows or on CPU mode. GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration. It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration. Add V1.3 model, which produces more natural restoration results, and better results on very low-quality / high-quality inputs.
    Downloads: 61 This Week
    Last Update:
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  • 6
    Video-subtitle-extractor

    Video-subtitle-extractor

    A GUI tool for extracting hard-coded subtitle (hardsub) from videos

    Video hard subtitle extraction, generate srt file. There is no need to apply for a third-party API, and text recognition can be implemented locally. A deep learning-based video subtitle extraction framework, including subtitle region detection and subtitle content extraction. A GUI tool for extracting hard-coded subtitles (hardsub) from videos and generating srt files. Use local OCR recognition, no need to set up and call any API, and do not need to access online OCR services such as Baidu and Ali to complete text recognition locally. Support GPU acceleration, after GPU acceleration, you can get higher accuracy and faster extraction speed. (CLI version) No need for users to manually set the subtitle area, the project automatically detects the subtitle area through the text detection model. Filter the text in the non-subtitle area and remove the watermark (station logo) text.
    1 Review
    Downloads: 59 This Week
    Last Update:
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  • 7
    Basic Pitch

    Basic Pitch

    A lightweight audio-to-MIDI converter with pitch bend detection

    Basic Pitch is a Python library for Automatic Music Transcription (AMT), using lightweight neural network developed by Spotify's Audio Intelligence Lab. It's small, easy-to-use, pip install-able and npm install-able via its sibling repo. Basic Pitch may be simple, but it's is far from "basic"! basic-pitch is efficient and easy to use, and its multi pitch support, its ability to generalize across instruments, and its note accuracy compete with much larger and more resource-hungry AMT systems. Provide a compatible audio file and a basic-pitch will generate a MIDI file, complete with pitch bends. The basic pitch is instrument-agnostic and supports polyphonic instruments, so you can freely enjoy transcription of all your favorite music, no matter what instrument is used. Basic pitch works best on one instrument at a time.
    Downloads: 55 This Week
    Last Update:
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  • 8
    Open Notebook

    Open Notebook

    An Open Source implementation of Notebook LM with more flexibility

    Open Notebook is an open-source, privacy-focused alternative to Google’s Notebook LM that gives users full control over their research and AI workflows. Designed to be self-hosted, it ensures complete data sovereignty by keeping your content local or within your own infrastructure. The platform supports 16+ AI providers—including OpenAI, Anthropic, Ollama, Google, and LM Studio—allowing flexible model choice and cost optimization. Open Notebook enables users to organize and analyze multi-modal content such as PDFs, videos, audio files, web pages, and Office documents. It combines full-text and vector search with context-aware AI chat to deliver insights grounded in your own research materials. With advanced features like multi-speaker podcast generation, customizable content transformations, and a comprehensive REST API, Open Notebook provides a powerful and extensible research environment.
    Downloads: 38 This Week
    Last Update:
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  • 9
    EasyOCR

    EasyOCR

    Ready-to-use OCR with 80+ supported languages

    Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc. EasyOCR is a python module for extracting text from image. It is a general OCR that can read both natural scene text and dense text in document. We are currently supporting 80+ languages and expanding. Second-generation models: multiple times smaller size, multiple times faster inference, additional characters and comparable accuracy to the first generation models. EasyOCR will choose the latest model by default but you can also specify which model to use. Model weights for the chosen language will be automatically downloaded or you can download them manually from the model hub. The idea is to be able to plug-in any state-of-the-art model into EasyOCR. There are a lot of geniuses trying to make better detection/recognition models, but we are not trying to be geniuses here. We just want to make their works quickly accessible to the public.
    Downloads: 35 This Week
    Last Update:
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  • 10
    YOLOv3

    YOLOv3

    Object detection architectures and models pretrained on the COCO data

    Fast, precise and easy to train, YOLOv5 has a long and successful history of real time object detection. Treat YOLOv5 as a university where you'll feed your model information for it to learn from and grow into one integrated tool. You can get started with less than 6 lines of code. with YOLOv5 and its Pytorch implementation. Have a go using our API by uploading your own image and watch as YOLOv5 identifies objects using our pretrained models. Start training your model without being an expert. Students love YOLOv5 for its simplicity and there are many quickstart examples for you to get started within seconds. Export and deploy your YOLOv5 model with just 1 line of code. There are also loads of quickstart guides and tutorials available to get your model where it needs to be. Create state of the art deep learning models with YOLOv5
    Downloads: 31 This Week
    Last Update:
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  • 11
    deepface

    deepface

    A Lightweight Face Recognition and Facial Attribute Analysis

    DeepFace is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, ArcFace, Dlib, SFace and GhostFaceNet. Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.
    Downloads: 22 This Week
    Last Update:
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  • 12
    deepfakes_faceswap

    deepfakes_faceswap

    Deepfakes Software For All

    Faceswap is the leading free and open source multi-platform deepfakes software. When faceswapping was first developed and published, the technology was groundbreaking, it was a huge step in AI development. It was also completely ignored outside of academia because the code was confusing and fragmentary. It required a thorough understanding of complicated AI techniques and took a lot of effort to figure it out. Until one individual brought it together into a single, cohesive collection.
    Downloads: 22 This Week
    Last Update:
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  • 13
    Label Studio

    Label Studio

    Label Studio is a multi-type data labeling and annotation tool

    The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Detect objects on image, bboxes, polygons, circular, and keypoints supported. Partition image into multiple segments. Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models. The frontend part of Label Studio app lies in the frontend/ folder and written in React JSX. Multi-user labeling sign up and login, when you create an annotation it's tied to your account. Configurable label formats let you customize the visual interface to meet your specific labeling needs. Support for multiple data types including images, audio, text, HTML, time-series, and video.
    Downloads: 21 This Week
    Last Update:
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  • 14
    LSTMs for Human Activity Recognition

    LSTMs for Human Activity Recognition

    Human Activity Recognition example using TensorFlow on smartphone

    LSTM-Human-Activity-Recognition is a machine learning project that demonstrates how recurrent neural networks can be used to recognize human activities from sensor data. The repository implements a deep learning model based on Long Short-Term Memory (LSTM) networks to classify physical activities using time-series data collected from wearable sensors. The project uses the well-known Human Activity Recognition dataset derived from smartphone accelerometer and gyroscope signals. Through the use of sequential neural network architectures, the system learns patterns in motion data that correspond to activities such as walking, sitting, standing, or climbing stairs. The repository includes data preprocessing scripts, neural network architecture definitions, and training pipelines that allow researchers to reproduce and modify the experiments. It serves as an educational example of how deep learning models can process temporal sensor signals for pattern recognition tasks.
    Downloads: 20 This Week
    Last Update:
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  • 15
    Scikit Learn
    Machine Learning framework in Python
    3 Reviews
    Downloads: 146 This Week
    Last Update:
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  • 16
    Paperless-ngx

    Paperless-ngx

    A community-supported supercharged version of paperless

    Paperless-ngx is a community-supported open-source document management system that transforms your physical documents into a searchable online archive so you can keep, well, less paper.
    Downloads: 18 This Week
    Last Update:
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  • 17
    higgsfield

    higgsfield

    Fault-tolerant, highly scalable GPU orchestration

    Higgsfield is an open-source, fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters, such as Large Language Models (LLMs).
    Downloads: 14 This Week
    Last Update:
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  • 18
    KAIR

    KAIR

    Image Restoration Toolbox (PyTorch). Training and testing codes

    Image restoration toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSR/GAN, SwinIR.
    Downloads: 11 This Week
    Last Update:
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  • 19
    Fashion-MNIST

    Fashion-MNIST

    A MNIST-like fashion product database

    Fashion-MNIST is an open-source dataset created by Zalando Research that provides a standardized benchmark for image classification algorithms in machine learning. The dataset contains grayscale images of fashion products such as shirts, shoes, coats, and bags, each labeled according to its clothing category. It was designed as a direct replacement for the original MNIST handwritten digits dataset, maintaining the same structure and image size so that researchers could easily switch datasets without modifying their experimental pipelines. The dataset consists of 70,000 images in total, with 60,000 examples used for training and 10,000 reserved for testing. Each image has a resolution of 28 by 28 pixels and belongs to one of ten clothing classes, making it suitable for evaluating classification models. Because the dataset represents real-world objects rather than handwritten digits, it offers a more challenging benchmark for testing machine learning algorithms.
    Downloads: 10 This Week
    Last Update:
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  • 20
    MLflow

    MLflow

    Open source platform for the machine learning lifecycle

    MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud).
    Downloads: 10 This Week
    Last Update:
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  • 21
    DocTR

    DocTR

    Library for OCR-related tasks powered by Deep Learning

    DocTR provides an easy and powerful way to extract valuable information from your documents. Seemlessly process documents for Natural Language Understanding tasks: we provide OCR predictors to parse textual information (localize and identify each word) from your documents. Robust 2-stage (detection + recognition) OCR predictors with pretrained parameters. User-friendly, 3 lines of code to load a document and extract text with a predictor. State-of-the-art performances on public document datasets, comparable with GoogleVision/AWS Textract. Easy integration (available templates for browser demo & API deployment). End-to-End OCR is achieved in docTR using a two-stage approach: text detection (localizing words), then text recognition (identify all characters in the word). As such, you can select the architecture used for text detection, and the one for text recognition from the list of available implementations.
    Downloads: 9 This Week
    Last Update:
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  • 22
    spaCy models

    spaCy models

    Models for the spaCy Natural Language Processing (NLP) library

    spaCy is designed to help you do real work, to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive. spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. If your application needs to process entire web dumps, spaCy is the library you want to be using. Since its release in 2015, spaCy has become an industry standard with a huge ecosystem. Choose from a variety of plugins, integrate with your machine learning stack and build custom components and workflows.
    Downloads: 9 This Week
    Last Update:
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  • 23
    FID score for PyTorch

    FID score for PyTorch

    Compute FID scores with PyTorch

    This is a port of the official implementation of Fréchet Inception Distance to PyTorch. FID is a measure of similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network. The weights and the model are exactly the same as in the official Tensorflow implementation, and were tested to give very similar results (e.g. .08 absolute error and 0.0009 relative error on LSUN, using ProGAN generated images). However, due to differences in the image interpolation implementation and library backends, FID results still differ slightly from the original implementation. In difference to the official implementation, you can choose to use a different feature layer of the Inception network instead of the default pool3 layer.
    Downloads: 8 This Week
    Last Update:
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  • 24
    NanoDet-Plus

    NanoDet-Plus

    Lightweight anchor-free object detection model

    Super fast and high accuracy lightweight anchor-free object detection model. Real-time on mobile devices. NanoDet is a FCOS-style one-stage anchor-free object detection model which using Generalized Focal Loss as classification and regression loss. In NanoDet-Plus, we propose a novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) to solve the optimal label assignment problem in lightweight model training. We also introduce a light feature pyramid called Ghost-PAN to enhance multi-layer feature fusion. These improvements boost previous NanoDet's detection accuracy by 7 mAP on COCO dataset. NanoDet provide multi-backend C++ demo including ncnn, OpenVINO and MNN. There is also an Android demo based on ncnn library. Supports various backends including ncnn, MNN and OpenVINO. Also provide Android demo based on ncnn inference framework.
    Downloads: 8 This Week
    Last Update:
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  • 25
    TorchRL

    TorchRL

    A modular, primitive-first, python-first PyTorch library

    TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. TorchRL provides PyTorch and python-first, low and high-level abstractions for RL that are intended to be efficient, modular, documented, and properly tested. The code is aimed at supporting research in RL. Most of it is written in Python in a highly modular way, such that researchers can easily swap components, transform them, or write new ones with little effort.
    Downloads: 8 This Week
    Last Update:
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