Update README.md

README.md

@@ -4,4 +4,75 @@ language:
 - en
 base_model:
 - google/efficientnet-b0
----
+---
+
+# Fall Detection Model using EfficientNetB0
+
+This model detects whether a person has **fallen** in an input image using transfer learning with **EfficientNetB0**. It is trained for binary classification: **Fall Detected** or **No Fall Detected**.
+
+---
+
+## Model Architecture
+
+- **Base Model**: EfficientNetB0 (`include_top=False`, pretrained on ImageNet)
+- **Top Layers**:
+  - GlobalAveragePooling2D
+  - BatchNormalization
+  - Dropout (0.4)
+  - Dense (sigmoid activation)
+- **Loss Function**: Binary Crossentropy
+- **Optimizer**: Adam
+
+The model was trained in two phases:
+- Initial training with base model frozen (10 epochs)
+- Fine-tuning with selective unfreezing (5 additional epochs)
+
+Data augmentation techniques like `RandomFlip`, `RandomRotation`, and `RandomZoom` are used during training.
+
+---
+
+## 📦 How to Use
+
+### 1. Load the Model from Hugging Face
+
+```python
+from huggingface_hub import from_pretrained_keras
+
+# Replace with your actual repo path
+model = from_pretrained_keras("your-username/your-model-name")
+```
+
+### 2. Run Inference on an Image
+
+```python
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.efficientnet import preprocess_input
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Define image size
+IMG_SIZE = (224, 224)
+
+# Load and preprocess the image
+img_path = "image_uri" # Your image uri (from the drive or local storage)
+img = image.load_img(img_path, target_size=IMG_SIZE)
+img_array = image.img_to_array(img)
+img_array = np.expand_dims(img_array, axis=0)
+img_array = preprocess_input(img_array)
+
+# Display the image
+plt.imshow(img)
+plt.axis("off")
+plt.show()
+
+# Make prediction
+prediction = model.predict(img_array)
+print(prediction)
+
+# Interpret prediction
+if prediction[0] < 0.15:
+    print("Prediction: 🚨 Fall Detected! 🚨")
+else:
+    print("Prediction: ✅ No Fall Detected.")
+```
+