| | --- |
| | pipeline_tag: image-classification |
| | --- |
| | # Model Card: Fine-Tuned InceptionV3 & Xception for Human Decomposition Image Classification |
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| | <!-- Provide a quick summary of what the model is/does. --> |
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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). |
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| | ## Model Details |
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| | ### Model Description |
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| | - **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. |
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| | ### Model Sources |
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| | - **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) |
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| | ## Usage |
| | The stage of decay classification is bodypart specific, that is, for the head, torso, or limbs. |
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