> This is the documentation for LibreYOLO 1.6.0. Current version: https://www.libreyolo.com/docs/models/quicksrnet

# QuickSRNet
QuickSRNet upsamples an image by a factor of two.
Tasks: Restoration. Install: pip install libreyolo.
Verified against LibreYOLO v1.6.0.

## Install

```bash
pip install "libreyolo"
```

## Predict

**Python**

```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**

```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 images
metrics = model.val(data="path/to/your/restore.yaml", workers=0)
print(metrics)
```

Use the dataset format for this task. [Validation](/docs/v1.6.0/train/validation) explains the dataset requirements and returned metrics.

## Export

| Task | ONNX | TorchScript | ExecuTorch | TensorRT | OpenVINO | Paddle | MNN | RKNN | ncnn | TFLite | CoreML | Core AI |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Restoration | yes | yes | yes | yes | yes |  |  |  | yes |  |  |  |

A "yes" means the export is supported. An empty cell means the exporter refuses that combination.

**Python**

```python
from libreyolo import LibreYOLO, SAMPLE_IMAGE

model = LibreYOLO("LibreQuickSRNetm2-restore.pt", device="cpu")
model.export(format="onnx")
```

[Export setup](/docs/v1.6.0/export) lists format dependencies and loading exported artifacts.

## Checkpoints

| File | Input (px) | Task | Weights license |
| --- | --- | --- | --- |
| `LibreQuickSRNetm2-restore.pt` |  | Restoration | bsd-3-clause |

## Licensing

Check the license on the Hugging Face repository of the specific weights you download. That repository is authoritative and licenses are not always uniform across a family. This is a description of the licenses involved, not legal advice.

- Original work: QuickSRNet, Qualcomm
- Upstream license: BSD-3-Clause
- Upstream source: https://github.com/quic/aimet-model-zoo
- LibreYOLO code: MIT
- Weights: BSD-3-Clause, republished at https://huggingface.co/LibreYOLO
- Interpretation: The source and released weights retain the publisher's BSD-3-Clause terms.


## QuickSRNet architecture

- Medium 2x: [SVG](/diagrams/models/quicksrnet/m2-restore.svg), [interactive diagram](/diagrams/models/quicksrnet/m2-restore.html)