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---
license: mit
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.
---
The repository contains **two versions of the model**:
1. **Keras `.h5` model**
- Full model for general use on machines with standard computational capacity.
2. **TensorFlow Lite `.tflite` model**
- Optimized for mobile and edge devices with limited computing power.
---
## 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("author-username/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.")
```
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