Created Model Card

README.md

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+---
+language: en
+license: apache-2.0
+library_name: keras
+tags:
+- image-classification
+- tensorflow
+- efficientnet
+- computer-vision
+- cats-vs-dogs
+metrics:
+- accuracy
+- auc
+- precision
+- recall
+- f1
+pipeline_tag: image-classification
+---
+
+# Pet Classification with EfficientNetB0
+
+This repository contains a high-performance deep learning model designed to classify images into two categories: **Cats** and **Dogs**. The model leverages the **EfficientNetB0** architecture, utilizing Transfer Learning and specialized Fine-Tuning to achieve professional-grade metrics.
+
+## Model Performance
+
+Evaluated on a balanced test set of **5,000 images**, the model demonstrates exceptional stability and discriminative power:
+
+| Metric | Score |
+| :--- | :--- |
+| **Test Accuracy** | **97.48%** |
+| **AUC Score** | **0.9974** |
+| **Precision** | **96.77%** |
+| **Recall** | **0.9824** |
+| **F1-Score** | **0.9750** |
+
+### Confusion Matrix Highlights
+* **Total Correct:** 4,874 / 5,000 images.
+* **Sensitivity:** High recall for 'Dog' class (0.9824), ensuring minimal false negatives.
+* **Confidence:** Average Loss of **0.0651**, indicating high certainty in classifications.
+
+## Architecture & Training Strategy
+
+The model uses a multi-stage training pipeline to maximize the features learned from the ImageNet-pre-trained EfficientNetB0 base.
+
+### 1. Model Structure
+* **Base:** EfficientNetB0 (Functional)
+* **Pooling:** GlobalAveragePooling2D
+* **Normalization:** BatchNormalization for training stability.
+* **Dense Layers:** 256 units (ReLU) followed by a 2-unit Softmax output.
+* **Regularization:** Dropout (0.4) to ensure high generalization and prevent overfitting.
+
+### 2. Training Phases
+* **Phase 1 (Transfer Learning):** The base model was frozen, and only the custom classification head was trained (Learning Rate: 1e-3).
+* **Phase 2 (Fine-Tuning):** The top 40 layers of the EfficientNet base were unfrozen and trained with a reduced learning rate (1e-4) to refine high-level feature detection.
+
+## How to Use
+
+To use this model locally with the `.keras` file:
+
+```python
+import tensorflow as tf
+from tensorflow.keras.applications.efficientnet import preprocess_input
+import cv2
+import numpy as np
+
+# Load model
+model = tf.keras.models.load_model('efficientnetb0_pet_classifier_finetuned.keras')
+
+def predict(img_path):
+    img = cv2.imread(img_path)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = cv2.resize(img, (224, 224))
+    img = preprocess_input(np.expand_dims(img, axis=0))
+    
+    preds = model.predict(img)
+    return "Dog" if np.argmax(preds) == 1 else "Cat"