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README.md

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+---
+language: en
+license: apache-2.0
+model_name: version-RFB-320-int8.onnx
+tags:
+- validated
+- vision
+- body_analysis
+- ultraface
+---
+<!--- SPDX-License-Identifier: MIT -->
+
+# Ultra-lightweight face detection model
+
+## Description
+This model is a lightweight facedetection model designed for edge computing devices.
+
+## Model
+| Model | Download | Download (with sample test data) | ONNX version | Opset version |
+| ------------- | ------------- | ------------- | ------------- | ------------- |
+|version-RFB-320| [1.21 MB](models/version-RFB-320.onnx) | [1.92 MB](models/version-RFB-320.tar.gz) | 1.4 | 9 |
+|version-RFB-640| [1.51 MB](models/version-RFB-640.onnx) | [4.59 MB](models/version-RFB-640.tar.gz) | 1.4 | 9 |
+|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 |
+
+### Dataset
+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/).
+
+### Source
+You can find the source code [here](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB).
+
+### Demo
+Run [demo.py](demo.py) python scripts example.
+
+## Inference
+
+### Input
+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).
+
+### Preprocessing
+Given a path `image_path` to the image you would like to score:
+```python
+image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)
+image = cv2.resize(image, (320, 240))
+image_mean = np.array([127, 127, 127])
+image = (image - image_mean) / 128
+image = np.transpose(image, [2, 0, 1])
+image = np.expand_dims(image, axis=0)
+image = image.astype(np.float32)
+```
+
+### Output
+The model outputs two arrays `(1 x 4420 x 2)` and `(1 x 4420 x 4)` of scores and boxes.
+
+### Postprocessing
+In postprocessing, threshold filtration and [non-max suppression](dependencies/box_utils.py) are applied to the scores and boxes arrays.
+
+
+## Quantization
+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.
+
+
+### Prepare Model
+Download model from [ONNX Model Zoo](https://github.com/onnx/models).
+
+```shell
+wget https://github.com/onnx/models/raw/main/vision/body_analysis/ultraface/models/version-RFB-320.onnx
+```
+
+Convert opset version to 12 for more quantization capability.
+
+```python
+import onnx
+from onnx import version_converter
+model = onnx.load('version-RFB-320.onnx')
+model = version_converter.convert_version(model, 12)
+onnx.save_model(model, 'version-RFB-320-12.onnx')
+```
+
+### Model quantize
+
+```bash
+cd neural-compressor/examples/onnxrt/body_analysis/onnx_model_zoo/ultraface/quantization/ptq_static
+bash run_tuning.sh --input_model=path/to/model  \ # model path as *.onnx
+--dataset_location=/path/to/data \
+--output_model=path/to/save
+```
+
+## Contributors
+
+* [asiryan](https://github.com/asiryan)
+* [yuwenzho](https://github.com/yuwenzho) (Intel)
+* [ftian1](https://github.com/ftian1) (Intel)
+* [hshen14](https://github.com/hshen14) (Intel)
+
+## License
+MIT
+