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README.md

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+---
+language: en
+license: apache-2.0
+model_name: mnist-7.onnx
+tags:
+- validated
+- vision
+- classification
+- mnist
+---
+<!--- SPDX-License-Identifier: MIT -->
+
+# MNIST - Handwritten Digit Recognition
+
+## Description
+This model predicts handwritten digits using a convolutional neural network (CNN).
+
+## Model
+|Model|Download|Download (with sample test data)| ONNX version |Opset version|TOP-1 ERROR|
+|-----|:-------|:-------------------------------|:-------------|:------------|:------------|
+|MNIST|[27 kB](model/mnist-1.onnx)|[26 kB](model/mnist-1.tar.gz) |1.0  |1 |1.1% |
+|MNIST|[26 kB](model/mnist-7.onnx)|[26 kB](model/mnist-7.tar.gz) |1.2  |7 |1.1% |
+|MNIST|[26 kB](model/mnist-8.onnx)|[26 kB](model/mnist-8.tar.gz) |1.3  |8 |1.1% |
+|MNIST-12|[26 kB](model/mnist-12.onnx)|[26 kB](model/mnist-12.tar.gz) |1.9  |12 |1.1% |
+|MNIST-12-int8|[11 kB](model/mnist-12-int8.onnx)|[10 kB](model/mnist-12-int8.tar.gz) |1.9  |12 |1.1% |
+
+### Dataset
+The model has been trained on the popular [MNIST dataset](http://yann.lecun.com/exdb/mnist/).
+
+### Source
+The model is trained in CNTK following the tutorial [CNTK 103D: Convolutional Neural Network with MNIST](https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103D_MNIST_ConvolutionalNeuralNetwork.ipynb). Note that the specific architecture used is the model with alternating convolution and max pooling layers (found under the "Solution" section at the end of the tutorial).
+
+### Demo
+[Run MNIST in browser](https://microsoft.github.io/onnxjs-demo/#/mnist) - implemented by ONNX.js with MNIST version 1.2
+
+## Inference
+We used CNTK as the framework to perform inference. A brief description of the inference process is provided below:
+
+### Input
+Input tensor has shape `(1x1x28x28)`, with type of float32.
+One image at a time. This model doesn't support mini-batch.
+
+### Preprocessing
+Images are resized into (28x28) in grayscale, with a black background and a white foreground (the number should be in white). Color value is scaled to [0.0, 1.0].
+
+Example:
+```python
+import numpy as np
+import cv2
+
+image = cv2.imread('input.png')
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+gray = cv2.resize(gray, (28,28)).astype(np.float32)/255
+input = np.reshape(gray, (1,1,28,28)
+```
+
+### Output
+The likelihood of each number before [softmax](https://en.wikipedia.org/wiki/Softmax_function), with shape of `(1x10)`.
+
+### Postprocessing
+Route the model output through a softmax function to map the aggregated activations across the network to probabilities across the 10 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
+MNIST-12-int8 is obtained by quantizing MNIST-12 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/image_recognition/onnx_model_zoo/mnist/quantization/ptq/README.md) to understand how to use Intel® Neural Compressor for quantization.
+
+### Environment
+onnx: 1.9.0
+onnxruntime: 1.10.0
+
+### Prepare model
+```shell
+wget https://github.com/onnx/models/raw/main/vision/classification/mnist/model/mnist-12.onnx
+```
+
+### Model quantize
+```bash
+bash run_tuning.sh --input_model=path/to/model \  # model path as *.onnx
+--config=mnist.yaml \
+--output_model=path/to/save
+```
+
+## References
+* [Intel® Neural Compressor](https://github.com/intel/neural-compressor)
+
+## Contributors
+* [mengniwang95](https://github.com/mengniwang95) (Intel)
+* [airMeng](https://github.com/airMeng) (Intel)
+* [ftian1](https://github.com/ftian1) (Intel)
+* [hshen14](https://github.com/hshen14) (Intel)
+
+## License
+MIT
+