| --- |
| tags: |
| - tensorflow |
| - efficientnetv2 |
| - transfer-learning |
| license: mit |
| --- |
| |
| # Cat Breed Classification Model |
|
|
| ## Model Description |
| This model leverages **transfer learning** with the **EfficientNetV2-L** architecture as the backbone to classify images of **five different cat breeds**. The model is fine-tuned to identify distinct types of cats based on image features, and the later layers of the EfficientNetV2-L backbone are exposed and used to create embeddings. |
|
|
| The model is trained to classify the following five cat breeds: |
| - Domestic Short-Hair |
| - Siamese |
| - Maine Coon |
| - Bengal |
| - Ragdoll |
|
|
| EfficientNetV2-L is a state-of-the-art image classification model that provides a good balance of speed and accuracy while achieving high performance on image recognition tasks. |
|
|
| ## Intended Use |
| This model is designed for the classification of cat breeds in images. It can be used for: |
| - **Cat breed classification** in images of cats. |
| - **Feature extraction** via embeddings, which can be used for further analysis, clustering, or as a feature for other machine learning tasks. |
|
|
| ## How to Use |
| You can easily load this model and use it to classify cat images with the following code snippet: |
|
|
| ```python |
| import tensorflow as tf |
| from tensorflow.keras.models import load_model |
| import numpy as np |
| from tensorflow.keras.preprocessing import image |
| |
| # Load the pre-trained model |
| model = tf.keras.models.load_model("path_to_model") |
| |
| # Example image preprocessing |
| img_path = "path_to_image.jpg" |
| img = image.load_img(img_path, target_size=(128, 128)) # Resize to 128x128 |
| img_array = image.img_to_array(img) |
| img_array = np.expand_dims(img_array, axis=0) # Add batch dimension |
| img_array /= 255.0 # Normalize to [0, 1] |
| |
| # Predict the cat breed |
| predictions = model.predict(img_array) |
| |
| # Predict the cat breed |
| predictions = model.predict(img_array) |
| class_labels = ["Bengal", "Domestic Shorthair", "Maine Coon", "Ragdoll", "Siamese"] |
| predicted_breed = class_labels[np.argmax(predictions)] |
| |
| |
| predicted_breed = class_labels[np.argmax(predictions)] |
| |
| print(f"Predicted Cat Breed: {predicted_breed}") |
| |
| ``` |
|
|
| ## Training Data |
|
|
| This model was trained on the **[Cats Breed Dataset](https://www.kaggle.com/datasets/yapwh1208/cats-breed-dataset)**, available on Kaggle. |
| The dataset consists of labeled images for each breed, which were resized to 128x128 pixels for training. The images were also normalized to a [0, 1] range to match the input size required by EfficientNetV2-L. Data augmentation techniques such as random rotations, flips, and scaling were applied to increase model robustness and reduce overfitting. |
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|
