FCN

A dense per-pixel classifier that replaces a detector's fully connected layers with convolutions, so it outputs a full-resolution class map instead of boxes. LibreYOLO ships it for semantic segmentation only.

Tasks: semantic
Sizes: r50, r101 at 520 px
Install: pip install libreyolo
Support tier: Inference only, since v. Predict, validate and export only. Training features do not apply.
Upstream: FCN by PyTorch, BSD-3-Clause. Paper, source
Licenses: Code BSD-3-Clause, weights BSD-3-Clause. Commercial use

Install

FCN 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("LibreFCNr50.pt")result = model(SAMPLE_IMAGE, save=True) mask = result.semantic_maskprint(mask.data.shape)   # (H, W) class idsprint(mask.classes)      # sorted class ids present in the image

CLI

libreyolo predict model=LibreFCNr50.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True

Semantic segmentation returns one class id per pixel, not boxes, so result.semantic_mask carries a (H, W) array on .data and the list of class ids present in the image on .classes. conf, iou and max_det are accepted for API parity but have no effect: the model assigns a class to every pixel by argmax, with no confidence threshold or NMS step. See prediction for sources, streaming and result handling.

Variants

Two ResNet depths, both at a fixed 520 px input. The library's inference graph is torchvision's dilated-ResNet FCN, not the original paper's VGG-based FCN-8s network with skip connections.

LibreYOLO does not train FCN: train() raises NotImplementedError for this family, which the support tier above marks as inference only. The two published checkpoints are torchvision's own COCO-trained weights, converted for LibreYOLO's loader.

Validate

val() returns metrics/mIoU and metrics/pixel_accuracy, measured against any dataset in the format you trained on.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreFCNr50.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/mIoU"])print(metrics["metrics/pixel_accuracy"])

CLI

libreyolo val model=LibreFCNr50.pt data=my-dataset.yaml

Export

Task	ONNX	TorchScript	ExecuTorch	TensorRT	OpenVINO	Paddle	MNN	RKNN	ncnn	TFLite	CoreML	Core AI
semantic	semantic to ONNX: supported.	semantic to TorchScript: supported.	semantic to ExecuTorch: not supported	semantic to TensorRT: supported.	semantic to OpenVINO: supported.	semantic to Paddle: not supported	semantic to MNN: not supported	semantic to RKNN: not supported	semantic to ncnn: not supported	semantic to TFLite: not supported	semantic to CoreML: not supported	semantic to Core AI: not 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. Export lists the arguments every format accepts.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreFCNr50.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

CLI

libreyolo export model=LibreFCNr50.pt format=onnxlibreyolo export model=LibreFCNr50.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("LibreFCNr50.onnx")result = model(SAMPLE_IMAGE) print(result.semantic_mask.data.shape)

Checkpoints

Every published weight file for this family.

File	Input (px)	Weights license
semantic
LibreFCNr50.pt	520	bsd-3-clause
LibreFCNr101.pt	520	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: FCN, 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 and these weights can be used in commercial and closed-source products. It asks you to keep the copyright notice, license text and a non-endorsement clause with any copy you redistribute. LibreYOLO's inference graph is torchvision's dilated-ResNet FCN, not the original VGG-based FCN-8s skip-fusion network from the paper. The two published checkpoints are torchvision's official COCO-trained weights; their separate LibreYOLO Hugging Face mirrors carry BSD-3-Clause on an implied basis disclosed by torchvision rather than an explicit checkpoint-specific grant, and torchvision's own documentation notes that pretrained-model terms can depend on the training data, leaving that determination to the user.