QuickSRNet
QuickSRNet upsamples an image by a factor of two.
- Task
- restoration
- Ukuran
- m2 at 64 px
- Instalasi
pip install libreyolo- Tier dukungan
- Hanya inferensi, sejak v1.6.0. Hanya prediksi, validasi, dan ekspor. Fitur pelatihan tidak berlaku.
- Lisensi
- Kode MIT, bobot BSD-3-Clause. Penggunaan komersial
Install
pip install "libreyolo"Predict
from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreQuickSRNetm2-restore.pt", device="cpu")result = model(SAMPLE_IMAGE)result.restored.save("upscaled.png")The Medium 2x model preserves native input resolution and doubles each spatial dimension. Validation uses paired low/high-resolution images and reports PSNR and SSIM. ONNX supports dynamic spatial shapes; TorchScript uses a fixed canvas. Training is not supported.
Validate
from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreQuickSRNetm2-restore.pt", device="cpu")# Paired super-resolution dataset YAML: inputs/val low-res images, targets/val 2x high-res imagesmetrics = model.val(data="path/to/your/restore.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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Restoration | Restoration to ONNX: didukung | Restoration to TorchScript: didukung | Restoration to ExecuTorch: didukung | Restoration to TensorRT: didukung | Restoration to OpenVINO: didukung | Restoration to Paddle: tidak didukung | Restoration to MNN: tidak didukung | Restoration to RKNN: tidak didukung | Restoration to ncnn: didukung | Restoration to TFLite: tidak didukung | Restoration to CoreML: tidak didukung | Restoration to Core AI: tidak didukung |
from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreQuickSRNetm2-restore.pt", device="cpu")model.export(format="onnx")Export setup lists format dependencies and loading exported artifacts.
Checkpoints
| Berkas | Lisensi bobot |
|---|---|
| Restoration | |
| LibreQuickSRNetm2-restore.pt | bsd-3-clause |
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
- QuickSRNet, Qualcomm
- Lisensi upstream
- BSD-3-Clause
- Sumber upstream
- github.com/quic/aimet-model-zoo
- Kode LibreYOLO
- MIT
- Bobot
- BSD-3-Clause, dipublikasikan ulang di huggingface.co/LibreYOLO
- Interpretasi
- The source and released weights retain the publisher's BSD-3-Clause terms.
