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pipeline_tag: image-classification
---
# Model Card: Fine-Tuned InceptionV3 & Xception for Human Decomposition Image Classification

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These CNN models were developed for the classification of human decomposition images into various stage of decay categories, including fresh, early decay, 
advanced decay, and skeletonized (Megyesi et al., 2005).

## Model Details

### Model Description

- **Developed by:** Anna-Maria Nau
- **Funded by:** National Institute of Justice
- **Model type:** CNNs for Image Classification
- **Base Model:** InceptionV3 and Xception pretrained on ImageNet
- **Transfer Learning Method:** Two-step transfer learning: (1) freeze all pre-trained convolutional layers of the base model and train newly added classifier layers on custom dataset and (2) unfreeze all layers, and fine-tune model end-to-end on custom dataset.

### Model Sources

- **Paper :**
  - [Stage of Decay Estimation Exploiting Exogenous and Endogenous Image Attributes to Minimize Manual Labeling Efforts and Maximize Classification Performance](https://ieeexplore.ieee.org/abstract/document/10222106)
  - [Towards Automation of Human Stage of Decay Identification: An Artificial Intelligence Approach](https://arxiv.org/abs/2408.10414)

## Usage
The stage of decay classification is bodypart specific, that is, for the head, torso, or limbs.