Add README file

README.md

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 ---
+language: en
 license: apache-2.0
+library_name: tensorflow
+tags:
+  - tensorflow
+  - keras
+  - emotion-recognition
+  - resnet50
+  - ckplus
+  - rafdb
+  - fine-tuning
+  - computer-vision
+  - deep-learning
+  - facial-expression
+  - affective-computing
+  - tflite
+model-index:
+  - name: emotion_resnet50_model
+    results:
+      - task:
+          type: image-classification
+        dataset:
+          type: dataset
+          name: CK+ & RAF-DB
+        metrics:
+          - name: accuracy
+            type: float
+            value: 0.8165
+    inference: "Supports TensorFlow and TensorFlow Lite inference"
 ---
+
+# 🧠 Emotion Recognition Model – ResNet50 (Fine-Tuned on CK+ and RAF-DB)
+
+## 📘 Overview
+
+This repository presents a **fine-tuned ResNet50-based Emotion Recognition model** trained on the **CK+** and **RAF-DB** facial expression datasets. The model classifies facial emotions into seven categories and provides high accuracy and generalization through a two-phase fine-tuning process. Both **TensorFlow** and **TensorFlow Lite** versions are available for deployment on cloud and edge devices.
+
+This model forms a key component of an **AI-powered, emotion-aware sign language translation system**, enabling real-time emotion recognition and integration into affective computing pipelines.
+
+---
+
+## 🧩 Model Architecture
+
+The architecture is built on **ResNet50**, pre-trained on ImageNet, and fine-tuned in two stages:
+
+1. **Stage 1 – Frozen Base Training (10 Epochs):**
+
+   - Convolutional layers frozen.
+   - Only top dense layers trained to learn task-specific emotion representations.
+
+2. **Stage 2 – Unfrozen Base Fine-Tuning (30 Epochs):**
+
+   - Entire base unfrozen with a lower learning rate.
+   - Enables deep adaptation to emotion-related facial features.
+
+---
+
+## 📊 Datasets
+
+1. **[CK+ Dataset (Kaggle)](https://www.kaggle.com/datasets/shareef0612/ckdataset)**
+2. **[RAF-DB Dataset (Kaggle)](https://www.kaggle.com/datasets/shuvoalok/raf-db-dataset)**
+
+### Dataset Preparation
+
+- Combined CK+ and RAF-DB datasets to increase emotion diversity.
+- Applied **extensive data augmentation** (rotation, flips, brightness, zoom, and shifts).
+- Ensured **balanced class distribution** post-augmentation.
+
+---
+
+## ⚙️ Training Configuration
+
+| Parameter         | Description                        |
+| ----------------- | ---------------------------------- |
+| **Base Model**    | ResNet50 (Pre-trained on ImageNet) |
+| **Optimizer**     | Adam                               |
+| **Learning Rate** | 1e-4 (unfrozen phase)              |
+| **Loss Function** | Sparse Categorical Crossentropy    |
+| **Batch Size**    | 32                                 |
+| **Epochs**        | 40 (10 + 30)                       |
+| **Image Size**    | 224x224                            |
+
+---
+
+## 📈 Performance Summary
+
+| Metric       | Training | Validation | Testing |
+| ------------ | -------- | ---------- | ------- |
+| **Accuracy** | 98.43%   | 86.05%     | 81.65%  |
+| **Loss**     | 0.0610   | 0.7748     | 1.1962  |
+
+### Classification Report
+
+| Class | Precision | Recall | F1-Score |
+| ----- | --------- | ------ | -------- |
+| 0     | 0.74      | 0.69   | 0.72     |
+| 1     | 0.53      | 0.49   | 0.51     |
+| 2     | 0.88      | 0.31   | 0.46     |
+| 3     | 0.93      | 0.90   | 0.91     |
+| 4     | 0.74      | 0.87   | 0.80     |
+| 5     | 0.77      | 0.75   | 0.76     |
+| 6     | 0.82      | 0.83   | 0.82     |
+
+**Overall Accuracy:** 81.65%
+**Weighted F1-Score:** 0.81
+
+---
+
+## 🖼️ Visualizations
+
+### 1. Training Accuracy and Loss
+
+- **Graph 1:** Training and Validation Accuracy vs Epochs
+  ![Training Accuracy](images/Training_Accuracy.png)
+
+- **Graph 2:** Training and Validation Loss vs Epochs
+  ![Training Loss](images/Training_Loss.png)
+
+### 2. Dataset Distributions
+
+- **Graph 3:** Original Dataset Class Distribution
+  ![Original Dataset Distribution](images/Original_Dataset_Distribution.png)
+
+- **Graph 4:** Balanced (Augmented) Dataset Class Distribution
+  ![Balanced Dataset Distribution](images/Balanced_Dataset_Distribution.png)
+
+### 3. Evaluation Visuals
+
+- **Graph 5:** Multi-Class ROC Curves (AUC per class)
+  ![ROC Curves](images/ROC_Curves.png)
+
+- **Graph 6:** Confusion Matrix (Heatmap)
+  ![Confusion Matrix](images/Confusion_Matrix.png)
+
+- **Graph 7:** Sample Test Results (5 predictions per class)
+  ![Sample Test Results](images/Sample_Test_Results.png)
+
+These visualizations demonstrate consistent learning, balanced data, and strong class-wise recognition.
+
+---
+
+## 🧩 Model Files
+
+| File                                | Description                                                    |
+| ----------------------------------- | -------------------------------------------------------------- |
+| `emotion_resnet50_model.h5`         | Full TensorFlow model (≈273 MB)                                |
+| `emotion_resnet50_optimized.tflite` | Optimized TensorFlow Lite model (≈23 MB) for mobile deployment |
+
+---
+
+## 🧰 Inference Example
+
+```python
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# Load original model
+model_path = 'emotion_resnet50_model.h5'
+model = tf.keras.models.load_model(model_path)
+
+# Prepare input
+img = image.load_img('test_face.jpg', target_size=(224, 224))
+input_data = np.expand_dims(image.img_to_array(img) / 255.0, axis=0)
+
+# Run inference
+pred = model.predict(input_data)
+classes = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']
+print("Original Model Prediction:", classes[np.argmax(pred)])
+
+# Load and run TFLite optimized model
+tflite_model_path = 'emotion_resnet50_optimized.tflite'
+interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
+interpreter.allocate_tensors()
+input_index = interpreter.get_input_details()[0]['index']
+output_index = interpreter.get_output_details()[0]['index']
+interpreter.set_tensor(input_index, input_data.astype(np.float32))
+interpreter.invoke()
+output = interpreter.get_tensor(output_index)
+print("TFLite Model Prediction:", classes[np.argmax(output)])
+```
+
+---
+
+## 🚀 Key Features
+
+- Dual-dataset fine-tuning (CK+ + RAF-DB)
+- Data-balanced training via augmentation
+- High accuracy with stable generalization
+- Lightweight TensorFlow Lite version for edge devices
+- Ideal for integration in emotion-aware systems
+
+---
+
+## 🏷️ Tags
+
+`emotion-recognition` `resnet50` `facial-expression` `deep-learning` `tensorflow` `tflite` `ckplus` `rafdb` `computer-vision` `affective-computing` `fine-tuning`
+
+---
+
+## 📄 Citation
+
+```bibtex
+@misc{pasindu_sewmuthu_abewickrama_singhe_2025,
+	author       = { Pasindu Sewmuthu Abewickrama Singhe },
+	title        = { resnet50-emotion-recognition-ckplus-rafdb (Revision 3216038) },
+	year         = 2025,
+	url          = { https://huggingface.co/PSewmuthu/resnet50-emotion-recognition-ckplus-rafdb },
+	doi          = { 10.57967/hf/6653 },
+	publisher    = { Hugging Face }
+}
+```
+
+---
+
+## 👤 Author & Model Info
+
+**Author:** P.S. Abewickrama Singhe  
+**Developed with:** TensorFlow + Keras  
+**License:** Apache-2.0  
+**Date:** October 2025  
+**Email:** [[email protected]](mailto:[email protected])