U-Net

U-Net performs semantic segmentation with an encoder, skip connections and an upsampling decoder.

Task: semantic segmentation
Instalasi: pip install libreyolo
Tier dukungan: Didukung, sejak v1.6.0. Model pendukung yang dapat dilatih: CI dijaga tetap lulus, fitur ditambahkan saat ada kesempatan.
Lisensi: Kode MIT, bobot Apache-2.0. Penggunaan komersial

Install

bash

pip install "libreyolo"

Predict

Python

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreUNets-sem.pt", device="cpu")result = model(SAMPLE_IMAGE) mask = result.semantic_maskprint(mask.data.shape)   # (H, W) class idsprint(mask.classes)      # sorted class ids present in the image

Weights download from Hugging Face on first use and are cached locally. LibreUNets-sem.pt is a conversion of the Cityscapes UNet-S5-D16 checkpoint from mmsegmentation: the same-padded S5-D16 graph with an FCN head, predicting the 19 Cityscapes classes. Prediction runs the whole frame on the 1024x2048 evaluation canvas.

Train

Use a semantic dataset with image/mask pairs. Training defaults to 160 epochs, batch 4 and amp=False, with a (512, 1024) crop and (1024, 2048) evaluation canvas. The loss includes an auxiliary head. Export is not supported.

Dataset setup describes the required training data.

Python

from libreyolo import LibreUNet # Random initialization: this trains from scratch.model = LibreUNet(size="s", device="cpu")# Replace with your semantic segmentation dataset YAML (image/mask pairs).model.train(data="path/to/your/semantic_dataset.yaml", pretrained=False, epochs=1, device="cpu", workers=0)

Validate

Python

from libreyolo import LibreYOLO # Or a U-Net checkpoint you trained with model.train().model = LibreYOLO("LibreUNets-sem.pt", device="cpu")# Replace with your semantic segmentation dataset YAML (image/mask pairs).metrics = model.val(data="path/to/your/semantic_dataset.yaml", workers=0)print(metrics)

Use the dataset format for this task. Validation explains the dataset requirements and returned metrics.

Checkpoints

Berkas	Lisensi bobot
Semantic segmentation
LibreUNets-sem.pt	apache-2.0

Setiap berkas di atas saat ini tersedia di organisasi LibreYOLO dan diunduh saat pertama kali digunakan.

Licensing

Periksa lisensi di repositori Hugging Face untuk bobot tertentu yang diunduh. Setiap checkpoint di organisasi LibreYOLO memiliki lisensi, dan lisensinya tidak selalu sama dalam satu family. Repositori tersebut adalah sumber resmi. Ringkasan di bawah menjelaskan ketentuan yang berlaku saat halaman ini terakhir diverifikasi.

Ini adalah deskripsi lisensi yang terlibat, bukan nasihat hukum. Jika jawabannya penting secara komersial, baca sendiri lisensinya dan mintalah nasihat dari penasihat hukum Anda.

Karya asli: U-Net, U-Net authors
Lisensi upstream: Apache-2.0
Sumber upstream: github.com/open-mmlab/mmsegmentation
Kode LibreYOLO: MIT
Bobot: Apache-2.0, dipublikasikan ulang di huggingface.co/LibreYOLO
Interpretasi: Apache-2.0 is a permissive license, so this code and these weights can be used in commercial and closed-source products. It asks you to keep its license text and attribution notices with any copy you redistribute, and it grants a patent license. LibreYOLO's port follows mmsegmentation's Apache-2.0 UNet-S5-D16 implementation with an FCN head, and LibreUNets-sem.pt is a conversion of mmsegmentation's Cityscapes checkpoint, which upstream releases under Apache-2.0.