README.md

---
tags:
- Face Image Quality Assessment
- ediffiqa
- Quality Assessment
- Face Recognition
- onnx
- onnxslim
---

# eDifFIQA ONNX

ONNX exports of the [eDifFIQA](https://github.com/LSIbabnikz/eDifFIQA) face image quality models.

These models estimate the visual quality of a face image. They can be used before face recognition or face verification to filter low-quality face crops.

> [!NOTE]  
> Note: The models expect cropped/aligned face images.

## Export Details

Models were exported with `torch==2.10.0` and simplified with `onnxslim==0.1.93`. All models were exported with dynamic batch size support and ONNX opset 20.

## Available Models

| Variant    | File              | Size   |
|------------|-------------------|--------|
| eDifFIQA-T | `ediffiqa_t.onnx` | tiny   |
| eDifFIQA-S | `ediffiqa_s.onnx` | small  |
| eDifFIQA-M | `ediffiqa_m.onnx` | medium |
| eDifFIQA-L | `ediffiqa_l.onnx` | large  |

## Usage

```python
from pathlib import Path

import cv2
import numpy as np
import onnxruntime as ort


class EDifFIQAOnnx:

    def __init__(
        self,
        model_path: Path | str,
        input_size: tuple[int, int] = (112, 112),
        providers: list[str] | None = None,
    ) -> None:
        self.model_path = str(model_path)
        self.input_size = input_size

        self.session = ort.InferenceSession(
            self.model_path,
            providers=providers or ["CPUExecutionProvider"],
        )

        self.input_name = self.session.get_inputs()[0].name
        self.output_name = self.session.get_outputs()[0].name

    def infer(self, image_bgr: np.ndarray) -> float:
        """
        Run quality inference on a BGR-aligned face image.

        :param image_bgr: BGR face image.
        :return: Quality score.
        """
        tensor = self._preprocess(image_bgr)
        output = self.session.run([self.output_name], {self.input_name: tensor})[0]
        return float(np.squeeze(output))

    def _preprocess(self, image_bgr: np.ndarray) -> np.ndarray:
        """
        Preprocess image.

        :param image_bgr: BGR image.
        :return: Input tensor with shape (1, 3, 112, 112).
        """
        image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
        image_rgb = cv2.resize(image_rgb, self.input_size)

        image = image_rgb.astype(np.float32)
        image = ((image / 255.0) - 0.5) / 0.5
        image = np.transpose(image, (2, 0, 1))[None, ...]

        return image.astype(np.float32)
```