Create README.md

README.md

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+---
+license: mit
+tags:
+- computer-vision
+- image-classification
+- emotion-recognition
+- facial-expression-recognition
+- cnn
+- keras
+- tensorflow
+pipeline_tag: image-classification
+---
+
+# Face Emotion Recognition CNN (v2)
+
+## Model Description
+Face Emotion Recognition CNN (v2) is a lightweight Convolutional Neural Network designed to classify facial expressions into seven basic emotion categories using grayscale facial images of size 48×48. The model is optimized for fast inference and practical deployment in real-time and resource-constrained environments.
+
+The model was trained using the Adam optimizer and categorical cross-entropy loss, balancing performance and computational efficiency.
+
+## Intended Use
+This model is intended for:
+- Facial emotion recognition from static images
+- Human–computer interaction systems
+- Emotion-aware applications
+- Academic research and educational projects
+
+### Out-of-Scope Use
+The model should not be used for:
+- Surveillance, biometric identification, or user profiling
+- Medical, psychological, or legal decision-making
+- Any high-risk or safety-critical applications
+
+## Model Architecture
+- Architecture Type: Convolutional Neural Network (CNN)
+- Input: Grayscale facial images (48 × 48)
+- Output: Probability distribution over 7 emotion classes
+- Optimizer: Adam
+- Loss Function: Categorical Crossentropy
+
+## Training Configuration
+- Image Size: 48 × 48  
+- Batch Size: 64  
+- Epochs: 40  
+- Learning Rate: 0.0005  
+- Number of Classes: 7  
+- Model Name: face_emotion_cnn_v2  
+
+## Emotion Labels
+| Label ID | Emotion   |
+|--------:|-----------|
+| 0 | Angry |
+| 1 | Disgust |
+| 2 | Fear |
+| 3 | Happy |
+| 4 | Neutral |
+| 5 | Sad |
+| 6 | Surprise |
+
+## Dataset
+The model was trained on a facial emotion dataset containing grayscale face images resized to 48×48 pixels. Images were preprocessed using normalization and face alignment where applicable. The dataset includes seven emotion categories.
+
+Note: Dataset composition and demographic distribution may influence model bias and generalization.
+
+## Evaluation
+The model was evaluated on a held-out test set with the following results:
+
+### Training Performance (Final Epoch)
+- Accuracy: 0.5976  
+- Loss: 1.1217  
+
+### Test Performance
+- Test Accuracy: 0.5942  
+
+These results reflect moderate performance, which is typical for compact CNN models trained on low-resolution facial emotion datasets.
+
+## Usage
+### Inference Example
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    task="image-classification",
+    model="your-username/face_emotion_cnn_v2"
+)
+
+predictions = classifier("face.jpg")
+print(predictions)