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---
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language: en
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license: apache-2.0
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model_name: version-RFB-320-int8.onnx
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tags:
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- validated
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- vision
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- body_analysis
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- ultraface
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---
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<!--- SPDX-License-Identifier: MIT -->
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# Ultra-lightweight face detection model
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## Description
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This model is a lightweight facedetection model designed for edge computing devices.
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## Model
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| Model | Download | Download (with sample test data) | ONNX version | Opset version |
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| ------------- | ------------- | ------------- | ------------- | ------------- |
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|version-RFB-320| [1.21 MB](models/version-RFB-320.onnx) | [1.92 MB](models/version-RFB-320.tar.gz) | 1.4 | 9 |
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|version-RFB-640| [1.51 MB](models/version-RFB-640.onnx) | [4.59 MB](models/version-RFB-640.tar.gz) | 1.4 | 9 |
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|version-RFB-320-int8| [0.44 MB](models/version-RFB-320-int8.onnx) | [1.2 MB](models/version-RFB-320-int8.tar.gz) | 1.14 | 12 |
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### Dataset
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The training set is the VOC format data set generated by using the cleaned widerface labels provided by [Retinaface](https://arxiv.org/pdf/1905.00641.pdf) in conjunction with the widerface [dataset](http://shuoyang1213.me/WIDERFACE/).
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### Source
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You can find the source code [here](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB).
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### Demo
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Run [demo.py](demo.py) python scripts example.
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## Inference
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### Input
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Input tensor is `1 x 3 x height x width` with mean values `127, 127, 127` and scale factor `1.0 / 128`. Input image have to be previously converted to `RGB` format and resized to `320 x 240` pixels for **version-RFB-320** model (or `640 x 480` for **version-RFB-640** model).
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### Preprocessing
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Given a path `image_path` to the image you would like to score:
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```python
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image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)
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image = cv2.resize(image, (320, 240))
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image_mean = np.array([127, 127, 127])
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image = (image - image_mean) / 128
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image = np.transpose(image, [2, 0, 1])
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image = np.expand_dims(image, axis=0)
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image = image.astype(np.float32)
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```
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### Output
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The model outputs two arrays `(1 x 4420 x 2)` and `(1 x 4420 x 4)` of scores and boxes.
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### Postprocessing
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In postprocessing, threshold filtration and [non-max suppression](dependencies/box_utils.py) are applied to the scores and boxes arrays.
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## Quantization
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version-RFB-320-int8 is obtained by quantizing fp32 version-RFB-320 model. We use [Intel® Neural Compressor](https://github.com/intel/neural-compressor) with onnxruntime backend to perform quantization. View the [instructions](https://github.com/intel/neural-compressor/blob/master/examples/onnxrt/body_analysis/onnx_model_zoo/ultraface/quantization/ptq_static/README.md) to understand how to use Intel® Neural Compressor for quantization.
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### Prepare Model
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Download model from [ONNX Model Zoo](https://github.com/onnx/models).
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```shell
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wget https://github.com/onnx/models/raw/main/vision/body_analysis/ultraface/models/version-RFB-320.onnx
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```
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Convert opset version to 12 for more quantization capability.
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```python
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import onnx
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from onnx import version_converter
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model = onnx.load('version-RFB-320.onnx')
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model = version_converter.convert_version(model, 12)
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onnx.save_model(model, 'version-RFB-320-12.onnx')
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```
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### Model quantize
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```bash
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cd neural-compressor/examples/onnxrt/body_analysis/onnx_model_zoo/ultraface/quantization/ptq_static
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bash run_tuning.sh --input_model=path/to/model \ # model path as *.onnx
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--dataset_location=/path/to/data \
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--output_model=path/to/save
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```
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## Contributors
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* [asiryan](https://github.com/asiryan)
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* [yuwenzho](https://github.com/yuwenzho) (Intel)
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* [ftian1](https://github.com/ftian1) (Intel)
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* [hshen14](https://github.com/hshen14) (Intel)
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## License
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MIT
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