
# 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.
{"license": "afl-3.0", "library_name": "keras", "tags": ["image-classification"], "datasets": ["mnist"], "metrics": ["accuracy"], "model-index": [{"name": "resnet_mnist_digits", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "MNIST", "type": "mnist"}, "metrics": [{"type": "accuracy", "value": 0.9945, "name": "Accuracy", "verified": false}]}]}]}
keras image-classification dataset:mnist license:afl-3.0 model-index region:us
TAGS
#keras #image-classification #dataset-mnist #license-afl-3.0 #model-index #region-us 


# 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.



## 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.
# 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. ## 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.
TAGS
#keras #image-classification #dataset-mnist #license-afl-3.0 #model-index #region-us 
 # 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. ## 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.
TAGS
#keras #image-classification #dataset-mnist #license-afl-3.0 #model-index #region-us 
# 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.## 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.