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README.md

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+---
+language: en
+license: mit
+tags:
+- tensorflow
+- keras
+- computer-vision
+- classroom-detection
+- background-validation
+- smart-attendance
+- mobilenetv1
+---
+
+# Smart Attendance - Background Validation Model
+
+This repository contains the background validation model used in the **Smart Attendance System**. It is designed to verify the background context of an attendance submission to ensure the check-in occurs within a valid classroom setting, preventing spoofing attempts where users check in from home, dorm rooms, or external environments.
+
+## Model Details
+- **Architecture**: MobileNetV1 base with classification head.
+- **Task**: Context/Background Verification
+- **Input Shape**: `(224, 224, 3)`
+- **Preprocessing**: 
+  - Image resized to `(224, 224)`.
+  - Preprocessed using standard MobileNet preprocessing (`tensorflow.keras.applications.mobilenet.preprocess_input`).
+- **Output**: Softmax/classification score representing class probabilities of the background environment.
+
+## How to Use
+To load and run inference in Python:
+
+```python
+import cv2
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.applications.mobilenet import preprocess_input
+
+# Load the model
+model = tf.keras.models.load_model("background_mobilenet_v1.h5")
+
+# Preprocessing
+def preprocess_background(img_crop: np.ndarray) -> np.ndarray:
+    img_resized = cv2.resize(img_crop, (224, 224))
+    img_batch = np.expand_dims(img_resized, axis=0)
+    return preprocess_input(img_batch.astype(np.float32))
+
+# Run inference
+input_tensor = preprocess_background(image)
+prediction = model.predict(input_tensor)
+```