#Model-training
Showing 26 of 26 repositories tagged #model-training, ranked by stars
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥
Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focused on training faces, objects, and styles.
Resource scheduling and cluster management for AI
Data-centric LLM training with dynamic sample selection, domain mixture optimization, and example reweighting inside the LLaMA-Factory training loop.
AI-Native & Cloud-Native FS: A high-performance file semantic layer for cloud object storage, integrated with high-speed cache
SkyChat是一款基于中文GPT-3 api的聊天机器人项目。它可以像chatGPT一样，实现人机聊天、问答、中英文互译、对对联、写古诗等任务。| SkyChat is a Chatbot project based on Chinese GPT3 API. Like chatGPT, it can do human-machine chat, question and answer, and can also complete tasks such as Chinese-English or English-Chinese translation, content continuation, couplets, and Chinese ancient poems writing.
Run Slurm in Kubernetes
SkyText是由奇点智源发布的中文GPT3预训练大模型，可以进行文章续写、对话、中英翻译、内容风格生成、推理、诗词对联等不同任务。| SkyText is a Chinese GPT3 pre-trained large model released by Singularity-AI, which can perform different tasks such as chatting, Q&A, and Chinese-English translation.
A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.
PyTorch dataset debugger for computer vision — pause training, mine live loss signals to surface mislabels, class imbalance & outliers, then curate your image, video & LiDAR data without restarting
PyTorch model training and layer saturation monitor
Accelerating AI Training and Inference from Storage Perspective (Must-read Papers on Storage for AI)
Give your computer an AI Brain
(Google Play Store App) Pipeline to convert real-life chess boards into 2D digital format (FEN) from images and live camera feeds. The system has two versions: a real-time pipeline using OAK-D Lite and a high-precision static-image pipeline. RF-DETR and YOLO is used to detect chess pieces, and traditional cv methods determine board positioning.
A ready-to-use notebook!
Extension for CSGO game: a real-time object detection extension using a trained YOLOv8 model with TensorRT-accelerated inference to enhance aiming precision within Counter-Strike: Global Offensive (CSGO).
A comprehensive, professional guide explaining the differences, strengths, and best practices of Retrieval-Augmented Generation (RAG) and Fine-Tuning for LLMs, including workflows, comparisons, decision frameworks, and real-world hybrid AI use cases.
📄 Enhance Apple Developer docs by converting them into AI-readable Markdown for easier access and improved usability.
🔬 Reproducible sandbox for Gaussian Naive Bayes (GNB) applied to cancer cell classification — includes an interactive notebook, data layout and preprocessing guidance, feature-extraction tips, a lightweight scikit-learn pipeline, evaluation protocols for small/imbalanced biomedical datasets, and example scripts for prepare/train/evaluate.
🩺 Machine Learning diabetes prediction model using Support Vector Machine (SVM) classifier. Analyzes 8 medical features (glucose, BMI, age, etc.) from Pima Indian dataset to predict diabetes risk with 75-80% accuracy. Built with Python, scikit-learn, pandas. Includes data preprocessing, model training, and prediction system for diabetes..
🩺 Advanced neural network for breast cancer classification using Wisconsin dataset. Analyzes cell nucleus characteristics from FNA samples to distinguish malignant/benign masses with 96.5% accuracy. Features comprehensive documentation, automated setup, testing framework, and deployment guides. Educational ML project with 15,000+ lines of docs.
👨 K-Means Clustering Customer Segmentation is an interactive Streamlit app that uses machine learning to group customers by income and spending habits. It helps businesses target marketing, personalize offers, and gain insights with easy retraining, visualizations, and modular code.
🍾 A comprehensive machine learning project using Random Forest algorithm to predict wine quality based on physicochemical properties. Features EDA, model training, hyperparameter tuning, feature importance analysis, and detailed documentation.
🚀 Complete ML Project: Salary Prediction using Linear Regression & Streamlit. 95.6% accuracy, interactive web interface, clean dataset, pre-trained model. Perfect for learning ML, web development, and practical HR applications.
Explore a collection of Jupyter notebooks that guide you through various stages of the machine learning pipeline. From data analysis and feature engineering to model training and deployment, these notebooks provide practical insights for both beginners and experienced data enthusiasts. Let's dive into the world of data-driven decision-making! 📊🚀"
🚗 Predict car prices instantly with Linear & Lasso Regression! Built with Streamlit, scikit-learn, pandas & matplotlib. Compare models, explore data, and learn ML hands-on. Fast, open source, and easy to use for students & developers!