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--- |
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license: afl-3.0 |
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tags: |
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- image-classification |
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library_name: keras |
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datasets: |
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- mnist |
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metrics: |
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- accuracy |
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model-index: |
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- name: resnet_mnist_digits |
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results: |
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- task: |
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type: image-classification |
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name: Image Classification |
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dataset: |
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type: mnist |
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name: MNIST |
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metrics: |
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- type: accuracy |
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value: .9945 |
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name: Accuracy |
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verified: false |
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--- |
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# Model Card for resnet_mnist_digits |
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This model is is a Residual Neural Network (ResNet) for classifying handwritten digits in the MNIST dataset. |
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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). |
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## Model Details |
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### Model Description |
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This model takes as an input a 28x28 array of MNIST digits with values normalized to [0, 1]. |
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The model was trained using Keras on an Nvidia Ampere A100. |
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- **Developed by:** Phillip Allen Lane |
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- **Model type:** ResNet |
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- **License:** afl-3.0 |
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### How to Get Started with the Model |
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Use the code below to get started with the model. |
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```py |
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from tensorflow.keras import models |
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from tensorflow.keras.datasets import mnist |
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from tensorflow.keras.utils import to_categorical |
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from keras.utils.data_utils import get_file |
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# load the MNIST dataset test images and labels |
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(_, _), (test_images, test_labels) = mnist.load_data() |
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# normalize the images |
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test_images = test_images.astype('float32') / 255 |
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# create one-hot labels |
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test_labels_onehot = to_categorical(test_labels) |
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# download the model |
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model_path = get_file('/path/to/resnet_mnist_digits.hdf5', 'https://huggingface.co/lane99/resnet_mnist_digits/resolve/main/resnet_mnist_digits.hdf5') |
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# import the model |
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resnet = models.load_model(model_path) |
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# evaluate the model |
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evaluation_conv = resnet.evaluate(test_images, test_labels_onehot) |
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print("Accuracy: ", str(evaluation_conv[1])) |
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``` |
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## Training Details |
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### Training Data |
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This model was trained on the 60,000 entries in the MNIST training dataset. |
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### Training Procedure |
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This model was trained with a 0.1 validation split for 15 epochs using a batch size of 128. |
