Create README.md

README.md

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+---
+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 creation of embeddings of cat breeds in images.
+
+## 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)