YOLOv3

YOLOv3 is the Darknet-53 detector that added multi-scale prediction and independent logistic classifiers to the YOLO line. LibreYOLO carries it as a frozen, inference-only exhibit in tiny, base and SPP sizes.

Tasks: detection
Sizes: t, b, spp at 416 to 608 px
Install: pip install libreyolo
Support tier: Museum, since v. Frozen exhibit. Bug fixes only.
Upstream: YOLOv3 by Joseph Redmon, Public domain (Darknet "YOLO LICENSE"). Paper, source
Licenses: Code MIT, weights Public domain (Darknet "YOLO LICENSE"). Commercial use

Install

YOLOv3 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("LibreYOLO3b.pt")result = model(SAMPLE_IMAGE, save=True) for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

CLI

libreyolo predict model=LibreYOLO3b.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True

SPP size

from libreyolo import LibreYOLO, SAMPLE_IMAGE # The SPP variant adds a spatial pyramid pooling block before the# detection heads and runs at its own native input size.model = LibreYOLO("LibreYOLO3spp.pt")result = model(SAMPLE_IMAGE)

The returned Results object is the one every family returns, so swapping in a different detector is a one line change. conf filters the confidence threshold and iou the NMS threshold, applied per scale before boxes from all three heads are merged. 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 any dataset in the format you validate on.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreYOLO3b.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/mAP50-95"])print(metrics["metrics/mAP50"])

CLI

libreyolo val model=LibreYOLO3b.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("LibreYOLO3b.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

CLI

libreyolo export model=LibreYOLO3b.pt format=onnxlibreyolo export model=LibreYOLO3b.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("LibreYOLO3b.onnx")result = model(SAMPLE_IMAGE) print(result.boxes.xyxy)

Checkpoints

Every published weight file for this family.

File	Input (px)	Weights license
Detection
LibreYOLO3t.pt	416	other
LibreYOLO3b.pt	416	other
LibreYOLO3spp.pt	608	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: YOLOv3, 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.