| | --- |
| | 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("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.") |
| | ``` |
| |
|
| |
|
