YOLOv1

YOLOv1 is the original 2016 detector that gave the YOLO family its name: one convolutional network with a fully connected head predicts every box and class score in a single pass, with no anchor boxes. LibreYOLO carries it as a frozen, inference-only exhibit.

Tasks: detection
Sizes: t, b at 448 px
Install: pip install libreyolo
Support tier: Museum, since v. Frozen exhibit. Bug fixes only.
Upstream: YOLOv1 by Joseph Redmon, Public domain (Darknet "YOLO LICENSE"). Paper, source
Licenses: Code MIT, weights Public domain (Darknet "YOLO LICENSE"). Commercial use

Install

YOLOv1 needs no extra beyond the base package.

bash

pip install libreyolo

Predict

This family is inference-only: train() raises NotImplementedError, so this page has no Train section. Predict, validate and export are all supported. Weights download from Hugging Face on first use and are cached locally.

Python

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreYOLO1b.pt")result = model(SAMPLE_IMAGE, save=True) for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

CLI

libreyolo predict model=LibreYOLO1b.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True

The returned Results object is the one every family returns, so swapping in a different detector is a one line change. Two things are specific to this family. The published checkpoint is trained on Pascal VOC (2007+2012), not COCO, so box.cls indexes the 20 VOC categories (aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor) rather than the 80 COCO ones. And the fully connected detection head accepts one image at a time, so a list of sources is looped rather than run as a true batch. See prediction for sources, streaming and result handling.

Validate

val() returns a dictionary of metrics/ keys covering precision, recall, mAP 50 and mAP 50-95, measured against a dataset in the same VOC-style label space the checkpoint was trained on.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreYOLO1b.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/mAP50-95"])print(metrics["metrics/mAP50"])

CLI

libreyolo val model=LibreYOLO1b.pt data=my-dataset.yaml

Export

Task	ONNX	TorchScript	ExecuTorch	TensorRT	OpenVINO	Paddle	MNN	RKNN	ncnn	TFLite	CoreML	Core AI
Detection	Detection to ONNX: supported.	Detection to TorchScript: supported.	Detection to ExecuTorch: supported.	Detection to TensorRT: supported.	Detection to OpenVINO: supported.	Detection to Paddle: not supported	Detection to MNN: not supported	Detection to RKNN: not supported	Detection to ncnn: supported.	Detection to TFLite: not supported	Detection to CoreML: not supported	Detection to Core AI: supported.

An exported artifact loads back through LibreYOLO() on its file suffix, so a .onnx or .engine file behaves like a checkpoint and returns the same Results. Running the graph in a bare runtime, with no LibreYOLO installed, is also supported, but then preprocessing and postprocessing are yours to write.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreYOLO1b.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

CLI

libreyolo export model=LibreYOLO1b.pt format=onnxlibreyolo export model=LibreYOLO1b.pt format=tensorrt half=True

Use the exported file

from libreyolo import LibreYOLO, SAMPLE_IMAGE # The factory routes on the file suffix, so an exported artifact loads# like any checkpoint and returns the same Results object.model = LibreYOLO("LibreYOLO1b.onnx")result = model(SAMPLE_IMAGE) print(result.boxes.xyxy)

Checkpoints

Every published weight file for this family.

File	Input (px)	Weights license
Detection
LibreYOLO1b.pt	448	other

Every file above exists in the LibreYOLO org today and downloads on first use.

Licensing

Check the license on the Hugging Face repository of the specific weights you download. Every checkpoint in the LibreYOLO org carries one, and they are not always the same across a family. That repository is the authoritative source; the summary below describes what applied when this page was last verified.

This is a description of the licenses involved, not legal advice. If the answer matters commercially, read the licenses yourself and take your own counsel.

Original work: YOLOv1, Joseph Redmon
Upstream license: Public domain (Darknet "YOLO LICENSE")
Upstream source: github.com/pjreddie/darknet
LibreYOLO code: MIT
Weights: Public domain (Darknet "YOLO LICENSE"), republished at huggingface.co/LibreYOLO
Interpretation: Darknet's bundled NOTICE quotes its own license in full: "Darknet is public domain. Do whatever you want with it." That covers both the architecture and the pretrained weights LibreYOLO converts from it, with no attribution requirement and no restriction on commercial use. LibreYOLO's own code around this architecture is MIT. One thing to know before deploying this checkpoint: it is trained on Pascal VOC 2007+2012, not COCO, so it detects and names the 20 VOC categories rather than the 80 COCO ones.