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--- |
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license: mit |
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language: |
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- en |
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base_model: |
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- google/efficientnet-b0 |
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--- |
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# Fall Detection Model using EfficientNetB0 |
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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**. |
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--- |
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## Model Architecture |
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- **Base Model**: EfficientNetB0 (`include_top=False`, pretrained on ImageNet) |
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- **Top Layers**: |
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- GlobalAveragePooling2D |
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- BatchNormalization |
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- Dropout (0.4) |
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- Dense (sigmoid activation) |
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- **Loss Function**: Binary Crossentropy |
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- **Optimizer**: Adam |
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The model was trained in two phases: |
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- Initial training with base model frozen (10 epochs) |
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- Fine-tuning with selective unfreezing (5 additional epochs) |
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Data augmentation techniques like `RandomFlip`, `RandomRotation`, and `RandomZoom` are used during training. |
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--- |
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The repository contains **two versions of the model**: |
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1. **Keras `.h5` model** |
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- Full model for general use on machines with standard computational capacity. |
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2. **TensorFlow Lite `.tflite` model** |
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- Optimized for mobile and edge devices with limited computing power. |
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--- |
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## How to Use |
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### 1. Load the Model from Hugging Face |
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```python |
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from huggingface_hub import from_pretrained_keras |
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# Replace with your actual repo path |
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model = from_pretrained_keras("author-username/model-name") |
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``` |
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### 2. Run Inference on an Image |
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```python |
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from tensorflow.keras.preprocessing import image |
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from tensorflow.keras.applications.efficientnet import preprocess_input |
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import numpy as np |
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import matplotlib.pyplot as plt |
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# Define image size |
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IMG_SIZE = (224, 224) |
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# Load and preprocess the image |
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img_path = "image_uri" # Your image uri (from the drive or local storage) |
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img = image.load_img(img_path, target_size=IMG_SIZE) |
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img_array = image.img_to_array(img) |
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img_array = np.expand_dims(img_array, axis=0) |
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img_array = preprocess_input(img_array) |
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# Display the image |
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plt.imshow(img) |
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plt.axis("off") |
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plt.show() |
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# Make prediction |
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prediction = model.predict(img_array) |
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print(prediction) |
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# Interpret prediction |
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if prediction[0] < 0.15: |
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print("Prediction: 🚨 Fall Detected! 🚨") |
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else: |
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print("Prediction: ✅ No Fall Detected.") |
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``` |
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