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README.md

@@ -1,3 +1,62 @@
----
-license: cc-by-nc-sa-4.0
----
+# Sign Language Recognition Model
+
+This model recognizes sign language gestures using landmark data from hand, pose, and face keypoints.
+
+## Model Details
+
+- **Model Type**: Sign Language Recognition
+- **Framework**: TensorFlow/Keras
+- **Input**: Landmark sequences (x, y, z coordinates)
+- **Output**: Sign language class predictions
+- **Classes**: {processor.sign_count} different signs
+- **Parameters**: {model.count_params():,}
+
+## Model Architecture
+
+- **Input Shape**: {model.input_shape}
+- **Output Shape**: {model.output_shape}
+- **Max Sequence Length**: {config.max_len}
+- **Embedding Dimension**: {config.dim}
+
+## Training Details
+
+- **Epochs**: {config.epoch}
+- **Batch Size**: {config.batch_size}
+- **Learning Rate**: {config.lr}
+- **Weight Decay**: {config.weight_decay}
+- **Best Validation Loss**: {min(history.history['val_loss']):.4f}
+- **Best Validation Accuracy**: {max(history.history['val_accuracy']):.4f}
+
+## Usage
+
+```python
+import tensorflow as tf
+import pickle
+import numpy as np
+
+# Load the model
+model = tf.keras.models.load_model('model.h5')
+
+# Load the processor
+with open('processor.pkl', 'rb') as f:
+    processor = pickle.load(f)
+
+# Example inference
+# your_landmark_data should be preprocessed using the same processor
+predictions = model.predict(your_landmark_data)
+predicted_classes = np.argmax(predictions, axis=1)
+```
+
+## Files Description
+
+- `model.h5`: Complete Keras model (recommended for inference)
+- `model_weights.h5`: Model weights only
+- `processor.pkl`: Data processor for landmark preprocessing
+- `config.json`: Model configuration and metadata
+- `training_history.json`: Training metrics and history
+- `inference_example.py`: Example inference script
+- `requirements.txt`: Required dependencies
+
+## Requirements
+
+See `requirements.txt` for the complete list of dependencies.

config.json

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+{
+  "model_type": "sign_language_recognition",
+  "framework": "tensorflow",
+  "max_len": 384,
+  "dim": 192,
+  "num_classes": 60,
+  "dropout_start_epoch": 15,
+  "batch_size": 32,
+  "learning_rate": 0.0005,
+  "weight_decay": 0.1,
+  "epochs_trained": 200,
+  "input_shape": [
+    null,
+    384,
+    708
+  ],
+  "output_shape": [
+    null,
+    60
+  ],
+  "total_params": 1763418,
+  "sign_classes": {
+    "go": 0,
+    "hot": 1,
+    "dad": 2,
+    "yes": 3,
+    "no": 4,
+    "sick": 5,
+    "mom": 6,
+    "cut": 7,
+    "stuck": 8,
+    "outside": 9,
+    "talk": 10,
+    "arm": 11,
+    "up": 12,
+    "person": 13,
+    "can": 14,
+    "close": 15,
+    "face": 16,
+    "head": 17,
+    "mad": 18,
+    "wait": 19,
+    "eye": 20,
+    "hide": 21,
+    "home": 22,
+    "why": 23,
+    "quiet": 24,
+    "will": 25,
+    "glasswindow": 26,
+    "not": 27,
+    "fireman": 28,
+    "down": 29,
+    "child": 30,
+    "hesheit": 31,
+    "find": 32,
+    "jump": 33,
+    "where": 34,
+    "room": 35,
+    "look": 36,
+    "high": 37,
+    "hear": 38,
+    "now": 39,
+    "time": 40,
+    "open": 41,
+    "fall": 42,
+    "owie": 43,
+    "drop": 44,
+    "man": 45,
+    "give": 46,
+    "car": 47,
+    "fast": 48,
+    "bad": 49,
+    "have": 50,
+    "stairs": 51,
+    "who": 52,
+    "cry": 53,
+    "loud": 54,
+    "haveto": 55,
+    "water": 56,
+    "see": 57,
+    "police": 58,
+    "touch": 59
+  },
+  "created_at": "2025-07-10T07:14:08.417702"
+}

inference_example.py

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+import tensorflow as tf
+import pickle
+import numpy as np
+import pandas as pd
+
+def load_model_and_processor():
+    """Load the trained model and processor."""
+    # Load the complete model
+    model = tf.keras.models.load_model('model.h5')
+    
+    # Load the processor
+    with open('processor.pkl', 'rb') as f:
+        processor = pickle.load(f)
+    
+    return model, processor
+
+def predict_sign(model, processor, landmark_data):
+    """
+    Predict sign from landmark data.
+    
+    Args:
+        model: Loaded Keras model
+        processor: SignLanguageProcessor instance
+        landmark_data: DataFrame with columns ['frame', 'row_id', 'x', 'y', 'z']
+    
+    Returns:
+        predicted_class: Predicted sign class
+        confidence: Prediction confidence
+    """
+    # Process the landmark data
+    X, _ = processor.process_dataset(landmark_data)
+    
+    if len(X) == 0:
+        return None, 0.0
+    
+    # Make prediction
+    predictions = model.predict(X)
+    predicted_class = np.argmax(predictions, axis=1)[0]
+    confidence = np.max(predictions, axis=1)[0]
+    
+    # Convert back to sign name if mapping exists
+    if hasattr(processor, 'index_to_sign'):
+        sign_name = processor.index_to_sign[predicted_class]
+        return sign_name, confidence
+    
+    return predicted_class, confidence
+
+# Example usage
+if __name__ == "__main__":
+    # Load model and processor
+    model, processor = load_model_and_processor()
+    
+    # Example landmark data (replace with your actual data)
+    # landmark_data = pd.read_csv('your_landmark_data.csv')
+    
+    # Make prediction
+    # predicted_sign, confidence = predict_sign(model, processor, landmark_data)
+    # print(f"Predicted sign: {predicted_sign}, Confidence: {confidence:.3f}")
+    
+    print("Model and processor loaded successfully!")
+    print(f"Model input shape: {model.input_shape}")
+    print(f"Model output shape: {model.output_shape}")
+    print(f"Number of classes: {processor.sign_count}")

model.h5

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+size 21418472

model.weights.h5

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processor.pkl

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requirements.txt

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+tensorflow
+pickle
+numpy
+pandas

training_history.json

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