QuickSRNet
QuickSRNet upsamples an image by a factor of two.
- 任务
- restoration
- 尺寸
- m2 at 64 px
- 安装
pip install libreyolo- 支持层级
- 仅推理，自 v1.6.0 起。仅支持预测、验证和导出。训练相关的功能不适用。
- 许可
- 代码采用 MIT，权重采用 BSD-3-Clause。商用
Install
bash
pip install "libreyolo"Predict
Python
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
Python
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
| 任务 | ONNX | TorchScript | ExecuTorch | TensorRT | OpenVINO | Paddle | MNN | RKNN | ncnn | TFLite | CoreML | Core AI |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Restoration | Restoration to ONNX：支持 | Restoration to TorchScript：支持 | Restoration to ExecuTorch：支持 | Restoration to TensorRT：支持 | Restoration to OpenVINO：支持 | Restoration to Paddle：不支持 | Restoration to MNN：不支持 | Restoration to RKNN：不支持 | Restoration to ncnn：支持 | Restoration to TFLite：不支持 | Restoration to CoreML：不支持 | Restoration to Core AI：不支持 |
Python
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
| 文件 | 权重许可 |
|---|---|
| Restoration | |
| LibreQuickSRNetm2-restore.pt | bsd-3-clause |
上面的每个文件目前都在 LibreYOLO 组织中，并会在首次使用时下载。
Licensing
请检查你所下载的具体权重在 Hugging Face 仓库中的许可。LibreYOLO 组织里的每个检查点都附有许可，同一家族内也不一定相同。该仓库是权威来源；以下摘要说明本页上次验证时适用的情况。
这里只说明涉及的许可证，不构成法律意见。如果答案对商用很重要，请自行阅读许可证并咨询法律顾问。
- 原始工作
- QuickSRNet, Qualcomm
- 上游许可
- BSD-3-Clause
- LibreYOLO 代码
- MIT
- 权重
- 采用 BSD-3-Clause 许可，重新发布在 huggingface.co/LibreYOLO
- 解读
- The source and released weights retain the publisher's BSD-3-Clause terms.
