| pipeline_tag: image-classification | |
| # Model Card: Fine-Tuned InceptionV3 & Xception for Human Decomposition Image Classification | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| 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. | |