---
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.