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Readme.md

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+---
+tags:
+  - image-classification
+  - cats-vs-dogs
+  - tensorflow
+  - efficientnet
+pipeline_tag: image-classification
+library_name: tensorflow
+datasets:
+  - cats_vs_dogs
+license: mit       # or whichever license you prefer
+metrics:
+  - accuracy
+---
+
+# Cats vs Dogs — EfficientNetB0 Classifier
+
+This repository contains a convolutional neural network model trained to classify images of cats and dogs.  
+The model uses **EfficientNetB0 (pretrained on ImageNet)** as a base + custom classification head, and was trained on the `cats_vs_dogs` dataset via TensorFlow Datasets.  
+
+---
+
+## ✅ Model Details
+
+| Item | Description |
+|------|-------------|
+| **Base architecture** | EfficientNetB0 (pretrained, top layers removed) |
+| **Input shape** | 224 × 224 × 3 (RGB image) |
+| **Output** | Single sigmoid output — probability that the image is a “dog” |
+| **Training data** | cats_vs_dogs (split ~80% train / 20% validation) |
+| **Preprocessing** | Resize → 224×224, Normalize pixels to [0,1], optional data-augmentation |
+| **Loss / Optimizer** | `binary_crossentropy`, `Adam` |
+| **Training strategy** | Feature-extraction (base frozen) → Optional fine-tuning (unfreeze part of base) |
+| **Evaluation metric** | Accuracy (binary classification) |
+
+---
+
+## 📈 Performance (Your results — update after training)
+
+| Metric | Value |
+|-------|-------|
+| Validation accuracy (after feature-extraction) | ~0.5098…  |
+| Validation accuracy (after fine-tuning) | ~0.7052… |
+
+
+> ⚠️ These metrics depend on training/validation split, augmentation, fine-tuning. Consider re-training or cross-validation for better estimates.
+
+---
+
+## 💡 Inference / Usage Example
+
+```python
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing import image
+
+# Load model (assuming you saved as model.keras or .h5)
+model = tf.keras.models.load_model("path/to/your_model.keras")
+
+# Load and preprocess a new image
+img = image.load_img("path/to/image.jpg", target_size=(224, 224))
+img = image.img_to_array(img) / 255.0
+img = np.expand_dims(img, axis=0)
+
+# Predict
+prob = model.predict(img)[0][0]
+if prob >= 0.5:
+    print("Dog 🐶 — confidence:", prob)
+else:
+    print("Cat 🐱 — confidence:", 1 - prob)

cats_vs_dogs_model.keras

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