model-training

Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.

PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.

🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

[NeurIPS 2026] DataFlex: A Unified Benchmark and Evaluation Platform for Data-Centric Training of Large Language Models

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.

YOLOv3 training, preprocessing, validation, and inference for object detection in xView satellite imagery and the xView detection challenge.

Monitoring platform for ML teams building real‑world AI at scale.

Retired Python SDK for the shut-down Ultralytics HUB API, kept for reference; build new integrations on the Ultralytics Platform REST API

A script that can be used to capture various porn novels for machine learning / 一个可以用于抓取各类色情小说用于机器学习的脚本

PyTorch model training and layer saturation monitor

Fine-tune LLMs and ML models with automatic dataset conversion, hyperparameter sweeps, and custom RL environments

Training pipeline for an osu!mania 7k next-event model, designed as the upstream predictor for audio-driven 7k map generation systems.

This project combines interests in cryptography, deep learning, and cybersecurity, providing valuable insights into the challenges of using AI techniques for cryptanalysis.

This comprehensive guide provides a universal process for preparing your own speech datasets and training a custom Text-to-Speech (TTS) model.

MoltTrend Claw is a privacy-first, autonomous AI agent that monitors cryptocurrency trends, analyzes market narratives, and generates actionable reports with long-term memory.

Simplified Deep Image Matting training code with keras on tensorflow

Experiments with Model Training, Deployment & Monitoring

skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.

A complete pipeline: train in Google Colab → deploy a live app on Streamlit Community Cloud. The app UI is a dark "security command-center" theme (radar-sweep header, live risk gauge, color-coded verdicts).

A Streamlit-powered machine learning playground that automatically detects classification or regression tasks, builds pipelines with preprocessing, trains models interactively, and visualizes metrics using Plotly. Backward-compatible, fully responsive, and deployable on Streamlit Cloud or Docker.