RetinaNet

RetinaNet is a one-stage detector trained with focal loss, which down-weights easy negatives so a dense grid of anchors no longer needs a separate proposal stage to stay accurate. LibreYOLO ports the torchvision implementation for detection.

Tasks: detection
Sizes: r50, r50v2 at 800 px
Install: pip install libreyolo
Support tier: Inference only, since v. Predict, validate and export only. Training features do not apply.
Upstream: RetinaNet by PyTorch, BSD-3-Clause. Paper, source
Licenses: Code BSD-3-Clause, weights BSD-3-Clause. Commercial use

Install

RetinaNet needs no optional extra. Everything it imports is in the base install.

bash

pip install libreyolo

Predict

Weights download from Hugging Face on first use and are cached locally.

Python

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreRetinaNetr50v2.pt")result = model(SAMPLE_IMAGE, save=True) for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

CLI

libreyolo predict model=LibreRetinaNetr50v2.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. conf and iou set the confidence and NMS thresholds; RetinaNet keeps its upstream NMS step over the dense anchor grid. See prediction for sources, streaming and result handling.

Variants

Two sizes, both ResNet-50 with a feature pyramid: r50 is the original head, and r50v2 replaces it with a GroupNorm head and a wider P6 block fed from the backbone's last stage instead of the FPN output.

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 trained on.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreRetinaNetr50v2.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/mAP50-95"])print(metrics["metrics/mAP50"])

CLI

libreyolo val model=LibreRetinaNetr50v2.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: not supported	Detection to ExecuTorch: not supported	Detection to TensorRT: not supported	Detection to OpenVINO: not supported	Detection to Paddle: not supported	Detection to MNN: not supported	Detection to RKNN: not supported	Detection to ncnn: not supported	Detection to TFLite: not supported	Detection to CoreML: not supported	Detection to Core AI: not supported

RetinaNet exports to ONNX only, at batch size 1. RetinaNet resizes to a variable, aspect-preserved input, so LibreYOLO forces dynamic=True regardless of what is passed, to keep the graph valid for sources of different shapes. An exported .onnx file loads back through LibreYOLO() on its file suffix and returns the same Results.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreRetinaNetr50v2.pt")model.export(format="onnx", imgsz=800)

CLI

libreyolo export model=LibreRetinaNetr50v2.pt format=onnx imgsz=800

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("LibreRetinaNetr50v2.onnx")result = model(SAMPLE_IMAGE) print(result.boxes.xyxy)

Checkpoints

Every published weight file for this family.

File	Input (px)	Weights license
Detection
LibreRetinaNetr50.pt	800	bsd-3-clause
LibreRetinaNetr50v2.pt	800	bsd-3-clause

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: RetinaNet, PyTorch
Upstream license: BSD-3-Clause
Upstream source: github.com/pytorch/vision
LibreYOLO code: MIT
Weights: BSD-3-Clause, republished at huggingface.co/LibreYOLO
Interpretation: BSD-3-Clause is a permissive license, so this code can be used in commercial and closed-source products with no obligation on your own application code. It asks only that you keep the copyright notice and disclaimer with any copy you redistribute, and it carries no patent grant. The two published checkpoints used for parity testing are not distributed in the LibreYOLO source tree: torchvision's own documentation notes that a pretrained model's terms may depend on its training data, so each Hugging Face mirror ships the BSD text on that implied basis and repeats the caveat rather than issuing an explicit checkpoint-specific grant.