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