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Siddhartha276/
Fall_Detection

tflite

google/efficientnet-b0

finetune

mit

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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:

Keras .h5 model
- Full model for general use on machines with standard computational capacity.
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