mnist

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
MNIST	27 kB	26 kB	1.0	1
	26 kB	26 kB	1.2	7
	26 kB	26 kB	1.3	8

TOP-1 TEST ERROR RATE: 1.1%

Dataset

The model has been trained on the popular MNIST dataset.

Source

The model is trained in CNTK following the tutorial CNTK 103D: Convolutional Neural Network with MNIST. 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 - 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:

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, 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_*/.

License

MIT