AIOmarRehan/InceptionV3_Dogs_vs_Cats_Classifier_Model

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

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")