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resnet_mnist_digits

Image Classification
Keras
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metadata
license: afl-3.0
tags:
  - image-classification
library_name: keras
datasets:
  - mnist
metrics:
  - accuracy
model-index:
  - name: resnet_mnist_digits
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          type: mnist
          name: MNIST
        metrics:
          - type: accuracy
            value: 0.9945
            name: Accuracy
            verified: false

Model Card for resnet_mnist_digits

This model is is a Residual Neural Network (ResNet) for classifying handwritten digits in the MNIST dataset. This model has 27.5 M parameters and achieves 99.45% accuracy on the MNIST test dataset (i.e., on digits not seen during training).

Model Details

Model Description

This model takes as an input a 28x28 array of MNIST digits with values normalized to [0, 1]. The model was trained using Keras on an Nvidia Ampere A100.

  • Developed by: Phillip Allen Lane
  • Model type: ResNet
  • License: afl-3.0

How to Get Started with the Model

Use the code below to get started with the model.

from tensorflow.keras import models
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
from keras.utils.data_utils import get_file

# load the MNIST dataset test images and labels
(_, _), (test_images, test_labels) = mnist.load_data()

# normalize the images
test_images = test_images.astype('float32') / 255
# create one-hot labels
test_labels_onehot = to_categorical(test_labels)

# download the model
model_path = get_file('/path/to/resnet_mnist_digits.hdf5', 'https://huggingface.co/lane99/resnet_mnist_digits/resolve/main/resnet_mnist_digits.hdf5')
# import the model
resnet = models.load_model(model_path)

# evaluate the model
evaluation_conv = resnet.evaluate(test_images, test_labels_onehot)
print("Accuracy: ", str(evaluation_conv[1]))

Training Details

Training Data

This model was trained on the 60,000 entries in the MNIST training dataset.

Training Procedure

This model was trained with a 0.1 validation split for 15 epochs using a batch size of 128.