InceptionV3 Dogs vs Cats Classifier

• 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