PP-LiteSeg
PP-LiteSeg assigns a semantic class to each pixel using an STDC backbone and a fusion 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
pip install "libreyolo"Predict
from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibrePPLiteSegt50-sem.pt", device="cpu")result = model(SAMPLE_IMAGE)print(result.semantic_mask)The t50 and b50 variants evaluate at (512, 1024); t75 and b75 evaluate at (768, 1536). Dimensions are (height, width).
Train
Training defaults to 800 epochs, batch 8 and amp=False. The default crop is (512, 1024) for t50/b50 and (768, 768) for t75/b75. Validation keeps the native rectangle. The recipe combines cross-entropy, Dice and edge losses with auxiliary heads.
Dataset setup describes the required training data.
from libreyolo import LibreYOLO model = LibreYOLO("LibrePPLiteSegt50-sem.pt", device="cpu")# Semantic segmentation dataset YAML: images plus single-channel class-ID masks.model.train(data="path/to/your/semantic.yaml", epochs=1, device="cpu", workers=0)Validate
from libreyolo import LibreYOLO model = LibreYOLO("LibrePPLiteSegt50-sem.pt", device="cpu")# Semantic segmentation dataset YAML with 19 Cityscapes classes, like cityscapes.yaml.metrics = model.val(data="path/to/your/semantic.yaml", workers=0)print(metrics)Use the dataset format for this task. Validation explains the dataset requirements and returned metrics.
Export
| Task | ONNX | TorchScript | ExecuTorch | TensorRT | OpenVINO | Paddle | MNN | RKNN | ncnn | TFLite | CoreML | Core AI |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Semantic segmentation | Semantic segmentation to ONNX: didukung | Semantic segmentation to TorchScript: didukung | Semantic segmentation to ExecuTorch: tidak didukung | Semantic segmentation to TensorRT: didukung | Semantic segmentation to OpenVINO: didukung | Semantic segmentation to Paddle: tidak didukung | Semantic segmentation to MNN: tidak didukung | Semantic segmentation to RKNN: tidak didukung | Semantic segmentation to ncnn: tidak didukung | Semantic segmentation to TFLite: tidak didukung | Semantic segmentation to CoreML: tidak didukung | Semantic segmentation to Core AI: tidak didukung |
from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibrePPLiteSegt50-sem.pt", device="cpu")model.export(format="onnx")Export setup lists format dependencies and loading exported artifacts.
Checkpoints
| Berkas | Lisensi bobot |
|---|---|
| Semantic segmentation | |
| LibrePPLiteSegt50-sem.pt | apache-2.0 |
| LibrePPLiteSegb50-sem.pt | apache-2.0 |
| LibrePPLiteSegt75-sem.pt | apache-2.0 |
| LibrePPLiteSegb75-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
- PP-LiteSeg, PaddlePaddle
- Lisensi upstream
- Apache-2.0
- Sumber upstream
- github.com/PaddlePaddle/PaddleSeg
- Kode LibreYOLO
- MIT
- Bobot
- Apache-2.0, dipublikasikan ulang di huggingface.co/LibreYOLO
- Interpretasi
- The upstream implementation declares Apache-2.0. The STDC backbone also carries its original MIT attribution. The released checkpoints are converted from the Deci-AI/super-gradients Cityscapes artifacts; SuperGradients is released under Apache-2.0 and grants separate weight licenses only for YOLO-NAS, so these checkpoints carry Apache-2.0.
