{"repo":"ultralytics/yoloe_data_engine","free":true,"listed":false,"github":"https://github.com/ultralytics/yoloe_data_engine","clone":"git clone https://github.com/ultralytics/yoloe_data_engine.git","description":"YOLOE data pipeline for grounding and detection labels, predictions, text refinement, cache generation, and visualization.","language":"Python","stars":62,"topics":["computer-vision","data-pipeline","dataset-annotation","dataset-generation","grounding","label-refinement","object-detection","open-vocabulary","python","pytorch"],"license":"AGPL-3.0","category":"machine-learning","readme_excerpt":"🚀 YOLOE Data Engine The YOLOE Data Engine builds and refines training data for YOLOE, the real-time open-vocabulary model in the Ultralytics family. It closes the loop between a dataset and a model: existing labels are loaded, a trained YOLOE model predicts over the same images, and any prediction not already covered by a ground-truth box is merged back into the labels. The result is a denser, better-covered dataset for the next training run. The pipeline handles both grounding data (free-form text phrases tied to image regions, such as Flickr30k Entities or mixed-grounding GQA) and detection data (a fixed class vocabulary, such as Objects365), and it includes tooling to refine the text prompts attached to grounding labels and to visually inspect everything it produces. This is a working research repository: a collection of scripts rather than an installable package, with dataset, model, and buffer paths configured inside each script rather than passed on the command line. 🔧 Requirements A Python environment with the ultralytics package and its dependencies, plus PyTorch, NumPy, Pillow, OpenCV, and Matplotlib for the visualizers: There is no requirements.txt — the scripts import from ultralytics and are meant to run alongside a local Ultralytics checkout. A CUDA GPU is required for prediction; DataEngineAgent takes a list of devices and defaults to a single cuda:0 , while the example at the bottom of data engine agent.py spreads the work across four. ⚙️ How It Works The eng","default_branch":null,"files":null,"tree":[],"storefront":"/r/ultralytics","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ultralytics/yoloe_data_engine/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."}