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README.md

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+---
+language: en
+license: apache-2.0
+model_name: emotion-ferplus-12-int8.onnx
+tags:
+- validated
+- vision
+- body_analysis
+- emotion_ferplus
+---
+<!--- SPDX-License-Identifier: MIT -->
+
+# FER+ Emotion Recognition
+
+## Description
+This model is a deep convolutional neural network for emotion recognition in faces.
+
+## Model
+
+| Model          | Download | Download (with sample test data) | ONNX version | Opset version |
+|----------------|:-----------|:-----------|:--------|:-------------|
+|Emotion FERPlus |[34 MB](model/emotion-ferplus-2.onnx)|[31 MB](model/emotion-ferplus-2.tar.gz)|1.0|2|
+|Emotion FERPlus |[34 MB](model/emotion-ferplus-7.onnx)|[31 MB](model/emotion-ferplus-7.tar.gz)|1.2|7|
+|Emotion FERPlus |[34 MB](model/emotion-ferplus-8.onnx)|[31 MB](model/emotion-ferplus-8.tar.gz)|1.3|8|
+|Emotion FERPlus int8 |[19 MB](model/emotion-ferplus-12-int8.onnx)|[18 MB](model/emotion-ferplus-12-int8.tar.gz)|1.14|12|
+
+### Paper
+"Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution" [arXiv:1608.01041](https://arxiv.org/abs/1608.01041)
+
+### Dataset
+The model is trained on the FER+ annotations for the standard Emotion FER [dataset](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data), as described in the above paper.
+
+### Source
+The model is trained in CNTK, using the cross entropy training mode. You can find the source code [here](https://github.com/ebarsoum/FERPlus).
+
+### Demo
+[Run Emotion_FERPlus in browser](https://microsoft.github.io/onnxjs-demo/#/emotion_ferplus) - implemented by ONNX.js with Emotion_FERPlus version 1.2
+
+## Inference
+### Input
+The model expects input of the shape `(Nx1x64x64)`, where `N` is the batch size.
+### Preprocessing
+Given a path `image_path` to the image you would like to score:
+```python
+import numpy as np
+from PIL import Image
+
+def preprocess(image_path):
+input_shape = (1, 1, 64, 64)
+img = Image.open(image_path)
+img = img.resize((64, 64), Image.ANTIALIAS)
+img_data = np.array(img)
+img_data = np.resize(img_data, input_shape)
+return img_data
+```
+
+### Output
+The model outputs a `(1x8)` array of scores corresponding to the 8 emotion classes, where the labels map as follows:
+`emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, 'anger':4, 'disgust':5, 'fear':6, 'contempt':7}`
+### Postprocessing
+Route the model output through a softmax function to map the aggregated activations across the network to probabilities across the 8 classes.
+
+```python
+import numpy as np
+
+def softmax(scores):
+# your softmax function
+
+def postprocess(scores):
+'''
+This function takes the scores generated by the network and returns the class IDs in decreasing
+order of probability.
+'''
+prob = softmax(scores)
+prob = np.squeeze(prob)
+classes = np.argsort(prob)[::-1]
+return classes
+```
+### Sample test data
+Sets of sample input and output files are provided in
+* serialized protobuf TensorProtos (`.pb`), which are stored in the folders `test_data_set_*/`.
+
+## Quantization
+Emotion FERPlus int8 is obtained by quantizing fp32 Emotion FERPlus 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/emotion_ferplus/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/emotion_ferplus/model/emotion-ferplus-8.onnx
+```
+
+Convert opset version to 12 for more quantization capability.
+
+```python
+import onnx
+from onnx import version_converter
+model = onnx.load('emotion-ferplus-8.onnx')
+model = version_converter.convert_version(model, 12)
+onnx.save_model(model, 'emotion-ferplus-12.onnx')
+```
+
+### Model quantize
+
+```bash
+cd neural-compressor/examples/onnxrt/body_analysis/onnx_model_zoo/emotion_ferplus/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
+```
+
+## License
+MIT
+