Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+
+# InceptionV3 Dogs vs Cats Classifier
+
+This repository contains a **pre-trained TensorFlow/Keras model**:  
+
+- **File:** `InceptionV3_Dogs_and_Cats_Classification.h5`  
+- **Purpose:** Binary classification of cats vs dogs images  
+
+---
+
+## Model Details
+
+- **Architecture:** Transfer Learning using **InceptionV3** (pre-trained on ImageNet)  
+- **Custom Classification Head:**  
+  - Global Average Pooling  
+  - Dense layer (512 neurons, ReLU)  
+  - Dropout (0.5)  
+  - Dense layer with **Sigmoid** activation for binary classification  
+
+- **Input:** Images resized to **256 × 256** pixels  
+- **Output:** Probability of "Dog" class (values close to 1 indicate dog, close to 0 indicate cat)
+
+---
+
+## Performance
+
+- **Test Accuracy:** ~99%  
+- Confusion matrix and ROC curves indicate excellent classification performance  
+- Achieves near-perfect AUC (~1.0) on the test set  
+
+---
+
+## Usage Example
+
+```python
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+
+# Load the model
+model = load_model("InceptionV3_Dogs_and_Cats_Classification.h5")
+
+# Preprocess an image
+img = Image.open("cat_or_dog.jpg").resize((256, 256))
+img_array = np.expand_dims(np.array(img)/255.0, axis=0)
+
+# Predict
+prediction = model.predict(img_array)
+print("Dog" if prediction[0][0] > 0.5 else "Cat")