Create README.md

README.md

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+# Mask Detection Model
+
+## Model Overview
+**Name:** Mask Detection CNN  
+**Author:** Your Name  
+**Date:** 2025-09-26  
+**Framework:** Keras (TensorFlow backend)  
+**Format:** HDF5 (`.h5`)  
+**License:** MIT / CC0 (choose as needed)
+
+This model is designed to detect whether a person is wearing a mask or not from images of faces. It can be used in real-time applications such as webcam-based mask detection or image classification.
+
+---
+
+## Intended Use
+- **Primary Use:** Classify face images as "Mask" or "No Mask".  
+- **Applications:** Public safety, automated mask compliance monitoring, educational demos.  
+- **Limitations:**  
+  - The model works on cropped face images; it may produce inaccurate results if the input contains multiple faces without detection.  
+  - Lighting, occlusions, or extreme angles may affect accuracy.  
+  - Model trained on limited dataset; performance may vary on unseen ethnicities or environments.
+
+---
+
+## Model Details
+- **Input:** RGB image of shape `(128, 128, 3)`  
+- **Preprocessing:**  
+  - Resize to `128x128` pixels  
+  - Normalize pixel values to range `[0, 1]`  
+- **Output:**  
+  - `0`: Mask  
+  - `1`: No Mask  
+  - Output is a softmax probability distribution; prediction = `argmax(output)`  
+- **Architecture:** Convolutional Neural Network (CNN) with 2-3 Conv2D + MaxPooling layers, Flatten, Dense layers  
+
+---
+
+## Training
+- **Dataset:** Custom mask/no-mask face dataset  
+- **Loss Function:** Categorical Crossentropy  
+- **Optimizer:** Adam  
+- **Metrics:** Accuracy  
+- **Epochs:** Variable depending on training  
+- **Batch Size:** Variable depending on training  
+
+---
+
+## Evaluation
+- **Accuracy:** High on training/validation dataset (exact value depends on training)  
+- **Test Notes:** Recommended to evaluate on new faces under similar conditions to training images  
+
+---
+
+## Usage Example
+
+```python
+import cv2
+import numpy as np
+from keras.models import load_model
+
+# Load model
+model = load_model("mask_detection_model.h5")
+
+# Load image
+image = cv2.imread("face.jpg")
+image_resized = cv2.resize(image, (128, 128))
+image_scaled = image_resized.astype("float32") / 255.0
+image_input = np.expand_dims(image_scaled, axis=0)
+
+# Predict
+prediction = model.predict(image_input)
+pred_label = np.argmax(prediction, axis=1)[0]
+
+if pred_label == 0:
+    print("Mask 😷✅")
+else:
+    print("No Mask ❌")