Upload model_card.md

model_card.md

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+# Animal Recognition Model
+
+## Model Overview
+This model is designed to classify images of animals into predefined categories. It uses a ResNet50V2 base model and has been trained on a custom dataset.
+
+## Classes
+The model was trained on the following classes:
+- cat
+- dog
+- horse
+- lion
+- tiger
+- elephant
+
+## Usage
+1. Load the model using TensorFlow/Keras.
+2. Preprocess the input image to a size of 256x256 and normalize it.
+3. Pass the preprocessed image to the model for prediction.
+
+```python
+from keras.models import load_model
+import numpy as np
+from tensorflow.keras.utils import load_img, img_to_array
+
+def predict_image(image_path, model):
+    img = load_img(image_path, target_size=(256, 256))
+    img_array = img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    prediction = model.predict(img_array)
+    return np.argmax(prediction, axis=1)
+
+model = load_model('best_model.weights.h5')
+predicted_class = predict_image('/path/to/image.jpg', model)
+print(f"Predicted class: {predicted_class}")
+```
+
+## Training Details
+- **Base Model:** ResNet50V2 (pretrained on ImageNet)
+- **Dataset:** Custom animal dataset
+- **Optimizer:** Adam
+- **Loss Function:** Sparse Categorical Crossentropy
+- **Metrics:** Accuracy
+- **Augmentation:** Applied during training
+
+## Model Performance
+Training metrics and evaluation logs are available in the accompanying notebook.