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

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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)

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Performance

• Test Accuracy: ~99%
• Confusion matrix and ROC curves indicate excellent classification performance
• Achieves near-perfect AUC (~1.0) on the test set

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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")