{"repo":"iXab3r/YoloEase","free":true,"listed":false,"github":"https://github.com/iXab3r/YoloEase","clone":"git clone https://github.com/iXab3r/YoloEase.git","description":"is a tool that makes the process of training Yolo8+ models easier by leveraging automatic annotation capabilities using pre-trained model","language":"C#","stars":15,"topics":["automation","cv","gaming","ml","yolo"],"license":"MIT","category":"machine-learning","readme_excerpt":"YoloEase YoloEase is a desktop app for building YOLO object-detection models in short feedback loops. Start with a handful of manually annotated frames, get the first model quickly, then use that model to annotate larger batches. After a few iterations, most of the work becomes reviewing and fixing model suggestions instead of drawing every box from scratch. The result is a normal YOLO model: .pt weights for further training and .onnx weights for runtime use. EyeAuras ML Search is the main integration shown here, but the exported ONNX model is not EyeAuras-specific. Managed Training Setup YoloEase v2 removes most of the old environment setup chores. You do not have to start by installing Python, PyTorch, Ultralytics, CUDA packages, or ONNX export tools by hand. Open the Prerequisites tab, run Check all , then press Install missing . YoloEase manages: - portable Python for local training; - the project Python environment; - package installation tools; - PyTorch CPU or CUDA runtime; - Ultralytics YOLO CLI; - ONNX export and runtime tooling; - NVIDIA GPU detection and acceleration checks. If something goes wrong, the same page shows per-component diagnostics and copyable logs. Why It Exists Training a useful detector is rarely a one-shot process. You collect real frames, label a small part, train, inspect mistakes, add the frames where the model failed, and repeat. YoloEase keeps that loop inside one project: - create .yeproj projects with project-owned training assets; - prepar","default_branch":null,"files":null,"tree":[],"storefront":"/r/iXab3r","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/iXab3r/YoloEase/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}