Object Detection.
Unrestricted.
A modern training and inference engine for state-of-the-art YOLO models. Built for commercial applications, scientists, and the community.
1 from libreyolo import LibreYOLO, SAMPLE_IMAGE 2 3 model = LibreYOLO("LibreYOLOXs.pt") 4 results = model(SAMPLE_IMAGE, save=True)
What the Community Says
“This is really damn good! People need to take note of this!!!”
u/InternationalMany6
“I'll be so happy when there is a good, community maintained, MIT licensed alternative to Ultralytics.”
u/Covered_in_bees_
“This is so cool bro. If I see some place I can contribute, I will definitely do so!”
u/FedStan
Why LibreYOLO
Everything you need for object detection, nothing you don't.
Truly MIT
No AGPL anywhere in the dependency chain. Use it in closed-source products, SaaS, embedded systems — zero licensing risk.
One API, three architectures
YOLOX, YOLOv9, and RF-DETR behind a single LibreYOLO() call. Architecture, size, and class count auto-detected from weights.
Batteries included
Any input format — paths, URLs, PIL, NumPy, OpenCV, tensors, bytes. Tiled inference for large images. Auto-download weights from HuggingFace.
Train
Fine-tune on custom YOLO or COCO datasets with built-in augmentation, mixed precision, and early stopping. Resume from any checkpoint.
Validate
COCO-standard evaluation with mAP50, mAP50-95, precision, and recall on COCO or custom datasets. Per-class metrics and confusion matrix out of the box.
Export & deploy
One-line ONNX export with embedded metadata for easy deployment.
Deploy Anywhere
Export once, run on any hardware. From $35 boards to datacenter GPUs.
Export Formats
Tested Hardware
LibreYOLO vs Ultralytics
MIT means you own your work. No surprises.
Community Driven — built on the @testdummyvt fork that added RT-DETR + NMS-free YOLOv9 under the MIT license.