Update README.md

README.md

@@ -21,7 +21,9 @@ The model is trained to classify the following five cat breeds:
 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.
+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:
@@ -43,4 +45,21 @@ 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)
+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.