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README.md

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+---
+language: en
+license: mit
+library_name: keras
+pipeline_tag: image-classification
+tags:
+  - cat-emotion
+  - vgg16
+  - transfer-learning
+  - cnn
+  - tensorflow
+  - keras
+  - image-classification
+  - computer-vision
+  - depi
+datasets:
+  - custom
+metrics:
+  - accuracy
+---
+
+# 🐱 Cat Emotion Classification with VGG16
+
+A deep learning model for classifying cat emotions from images using **VGG16 transfer learning**, achieving **82.19% validation accuracy** across 5 emotion classes.
+
+## Model Description
+
+This model classifies cat facial expressions into one of five emotional categories. It uses VGG16 (pre-trained on ImageNet) as a feature extractor with a custom classification head featuring GlobalAveragePooling2D, BatchNormalization, and two Dense layers.
+
+**Part of:** Digital Egypt Pioneers Initiative (DEPI) Machine Learning Internship
+
+| Property | Value |
+|----------|-------|
+| **Architecture** | VGG16 + GAP + BN + Dense(512, 256) |
+| **Input Size** | 224×224×3 RGB |
+| **Output** | 5 classes (softmax) |
+| **Parameters** | ~15.3M (trainable: ~2.1M) |
+| **Framework** | TensorFlow / Keras |
+| **Accuracy** | 82.19% |
+
+## Classes
+
+| Index | Class | Description |
+|-------|-------|-------------|
+| 0 | `angry` | Cat displaying angry expressions |
+| 1 | `normal` | Cat in a neutral/normal state |
+| 2 | `rested` | Cat in a relaxed/resting state |
+| 3 | `sad` | Cat displaying sad expressions |
+| 4 | `surprised` | Cat displaying surprised expressions |
+
+## Quick Start
+
+```python
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
+
+# Load model
+model = load_model('best_vgg16_model.keras')
+
+# Predict
+img = Image.open('cat.jpg').resize((224, 224))
+img_array = np.expand_dims(np.array(img) / 255.0, axis=0)
+
+prediction = model.predict(img_array)
+classes = ['angry', 'normal', 'rested', 'sad', 'surprised']
+
+predicted_class = classes[np.argmax(prediction)]
+confidence = np.max(prediction)
+print(f"Emotion: {predicted_class} ({confidence:.2%})")
+```
+
+## Training Details
+
+### Architecture (V2 — Final)
+
+```
+VGG16 (ImageNet, last 8 layers unfrozen)
+    ↓
+GlobalAveragePooling2D
+    ↓
+BatchNormalization
+    ↓
+Dense(512, ReLU) → Dropout(0.5)
+    ↓
+BatchNormalization
+    ↓
+Dense(256, ReLU) → Dropout(0.3)
+    ↓
+Dense(5, Softmax)
+```
+
+### Training Strategy
+
+| Phase | Epochs | Learning Rate | Description |
+|-------|--------|--------------|-------------|
+| Phase 1 — Feature Extraction | 15 | 1e-4 | All VGG16 layers frozen |
+| Phase 2 — Fine-Tuning | 25 | 1e-5 | Last 8 VGG16 layers unfrozen |
+
+- **Optimizer:** Adam
+- **Loss:** Categorical Crossentropy
+- **Class Weights:** Balanced (computed with sklearn)
+- **Callbacks:** EarlyStopping (patience=5), ReduceLROnPlateau, ModelCheckpoint
+
+### Data Augmentation
+- Rotation: 30°
+- Width/Height shift: 0.25
+- Zoom: 0.25
+- Horizontal flip
+- Brightness: [0.8, 1.2]
+- Shear: 0.15
+
+## Performance
+
+### Model Comparison
+
+| Version | Input | Architecture | Accuracy |
+|---------|-------|-------------|----------|
+| V1 (Baseline) | 128×128 | Flatten → Dense(256) | 81.18% |
+| **V2 (Final)** | **224×224** | **GAP → BN → Dense(512,256)** | **82.19%** |
+
+### Dataset
+
+- **Training:** ~11,513 images
+- **Validation:** ~2,880 images
+- **Source:** [Kaggle — Cats Data Set](https://www.kaggle.com/datasets/bilalmahmoud/cats-data-set)
+
+## Files
+
+- `best_vgg16_model.keras` — Trained VGG16 model (V1, 134MB)
+- `README.md` — This model card
+
+## Intended Use
+
+- **Primary use:** Classifying cat emotions from images
+- **Users:** Researchers, students, pet tech developers
+- **Out of scope:** Other animal species, real-time production systems
+
+## Limitations
+
+- Trained on a specific cat emotion dataset — may not generalize to all cat breeds or lighting conditions
+- 5 emotion categories only — does not cover all possible feline emotional states
+- Best results with clear, well-lit frontal images of cats
+
+## Links
+
+- 🐱 **GitHub:** [Bolaal/Cat-Emotion-Classification-with-CNN](https://github.com/Bolaal/Cat-Emotion-Classification-with-CNN)
+- 📊 **Dataset:** [Kaggle — Cats Data Set](https://www.kaggle.com/datasets/bilalmahmoud/cats-data-set)
+
+## Citation
+
+```bibtex
+@misc{cat-emotion-vgg16-2025,
+  author = {Belal Mahmoud Hussien},
+  title = {Cat Emotion Classification with VGG16},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/Belall87/Cat-Emotion-Classification-with-CNN}
+}
+```
+
+## Author
+
+**Belal Mahmoud Hussien**
+- 📧 Email: belalmahmoud8787@gmail.com
+- 💼 LinkedIn: [belal-mahmoud-husien](https://linkedin.com/in/belal-mahmoud-husien)
+- 🐱 GitHub: [@Bolaal](https://github.com/Bolaal)
+- 🤗 Hugging Face: [@Belall87](https://huggingface.co/Belall87)

best_vgg16_model.keras

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config.json

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+{
+  "model_type": "vgg16-transfer-learning",
+  "architecture": "VGG16 + GlobalAveragePooling2D + BatchNorm + Dense(512,256)",
+  "framework": "tensorflow",
+  "library_name": "keras",
+  "input_shape": [224, 224, 3],
+  "num_classes": 5,
+  "class_labels": ["angry", "normal", "rested", "sad", "surprised"],
+  "preprocessing": {
+    "resize": [224, 224],
+    "rescale": 0.00392156862,
+    "color_mode": "rgb"
+  },
+  "training": {
+    "base_model": "VGG16",
+    "base_weights": "imagenet",
+    "unfrozen_layers": 8,
+    "optimizer": "adam",
+    "loss": "categorical_crossentropy",
+    "phase1_lr": 1e-4,
+    "phase2_lr": 1e-5,
+    "batch_size": 32,
+    "class_weights": true
+  },
+  "head": {
+    "layers": [
+      "GlobalAveragePooling2D",
+      "BatchNormalization",
+      "Dense(512, relu)",
+      "Dropout(0.5)",
+      "BatchNormalization",
+      "Dense(256, relu)",
+      "Dropout(0.3)",
+      "Dense(5, softmax)"
+    ]
+  },
+  "performance": {
+    "v1_accuracy": 0.8118,
+    "v2_accuracy": 0.8219,
+    "improvement": 0.0101
+  },
+  "dataset": {
+    "train_samples": 11513,
+    "val_samples": 2880,
+    "source": "https://www.kaggle.com/datasets/bilalmahmoud/cats-data-set"
+  }
+}

results_v2.json

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+{
+  "model_version": "V2",
+  "architecture": "VGG16 + GAP + BN + Dense(512,256)",
+  "input_size": "224x224x3",
+  "classes": [
+    "angry",
+    "normal",
+    "rested",
+    "sad",
+    "surprised"
+  ],
+  "num_classes": 5,
+  "training_samples": 11513,
+  "validation_samples": 2880,
+  "V1_accuracy": 0.8118,
+  "V2_accuracy": 0.8219,
+  "V2_loss": 0.3639,
+  "improvement": 0.0101,
+  "per_class_accuracy": {
+    "angry": 0.9677,
+    "normal": 0.6082,
+    "rested": 0.6611,
+    "sad": 0.8484,
+    "surprised": 0.9952
+  },
+  "training_phases": {
+    "phase1": {
+      "epochs": 15,
+      "lr": 0.0001,
+      "frozen": "all VGG16"
+    },
+    "phase2": {
+      "epochs": 25,
+      "lr": 1e-05,
+      "unfrozen": "last 8 layers"
+    }
+  }
+}