| language: en | |
| license: mit | |
| tags: | |
| - tensorflow | |
| - image-classification | |
| - mnist | |
| - digits | |
| datasets: | |
| - mnist | |
| metrics: | |
| - accuracy | |
| # Digit Recognition Model | |
| This model is trained to recognize handwritten digits from the MNIST dataset. | |
| ## Model Description | |
| - **Model Type:** CNN with Attention | |
| - **Task:** Image Classification | |
| - **Input:** 28x28 grayscale images | |
| - **Output:** Digit classification (0-9) | |
| ## Training | |
| The model was trained on the MNIST dataset using a CNN architecture with attention mechanisms. | |
| ## Usage | |
| ```python | |
| import tensorflow as tf | |
| import numpy as np | |
| # Load the model | |
| model = tf.saved_model.load("path_to_saved_model") | |
| # Prepare input | |
| image = tf.keras.preprocessing.image.load_img("digit.png", target_size=(28, 28)) | |
| image = tf.keras.preprocessing.image.img_to_array(image) | |
| image = image.astype('float32') / 255.0 | |
| image = np.expand_dims(image, axis=0) | |
| # Make prediction | |
| predictions = model(image) | |
| predicted_digit = tf.argmax(predictions, axis=1).numpy()[0] | |
| ``` | |