Update README.md

README.md

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 ---
 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: .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.
+
+```py
+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.