Digit Recognizer v1.0.0
This repository hosts the trained model for digit recognition in images. The model is a CNN-based architecture designed to classify images containing single digits between 0 and 9.
Model Details
- Architecture: A CNN model that classifies handwritten digits between 0 and 9.
- Dataset: Kaggle - MNIST dataset.
- Version: v1.0.0
- Task: Image Classification
- License: Apache 2.0
Usage
To use this model for inference, you can load it using the tensorflow
library.
Requires: Pip
# Clones the repository and installs dependencies
!git clone https://huggingface.co/preethamganesh/digit-recognizer-v1.0.0
!pip install tensorflow
# Imports TensorFlow
import tensorflow as tf
# Loads the pre-trained model from the cloned directory
model_path = "digit-recognizer-v1.0.0"
exported_model = tf.saved_model.load(model_path)
# Retrieves the default serving function from the loaded model
model = exported_model.signatures["serving_default"]
# Prepares a dummy input tensor for inference (batch size: 1, height: 28, width: 28, channels: 1)
input_data = tf.ones((1, 28, 28, 1), dtype=tf.float32)
# Performs inference using the model. The output will be a dictionary, with the classification logits in the key 'output_0'
output = model(input_data)["output_0"]
# Prints the predicted class (e.g., 0 for normal, 1 for abnormal)
predicted_digit = tf.argmax(output, axis=-1).numpy()[0]
print("Predicted digit: ", predicted_digit)
Training Details
Compute
- The model was trained on a GeForce 4070Ti GPU with 16GB VRAM.
- Training completed in approximately 20.3 seconds over 9 epochs.
Dataset
- The model was trained on the Kaggle - MNIST dataset, which includes images containing digits between 0 - 9.
Performance on test set
- Accuracy: 0.985
Citation
If you use this model in your research, please cite the repository:
@misc{preethamganesh2024digitrecog,
title={Digit Recognizer - v1.0.0},
author={Preetham Ganesh},
year={2025},
url={https://huggingface.co/preethamganesh/digit-recognizer-v1.0.0},
note={Apache-2.0 License}
}
Contact
For any questions or support, please contact [email protected].
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