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pipeline_tag: image-classification |
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# Model Card: Fine-Tuned InceptionV3, Xception, & ResNet50 for Human Decomposition Image Classification |
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These CNN models were developed for the classification of human decomposition images into six stages of human decay defined by Gelderman et al. (2018). |
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## Model Details |
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### Model Description |
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- **Developed by:** Anna-Maria Nau |
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- **Funded by:** National Institute of Justice |
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- **Model type:** CNNs for Image Classification |
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- **Base Model:** InceptionV3, Xception, and ResNet50 pretrained on ImageNet |
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- **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 :** |
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- [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) |
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- [Towards Automation of Human Stage of Decay Identification: An Artificial Intelligence Approach](https://arxiv.org/abs/2408.10414) |
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## Usage |
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The stage of decay classification is bodypart specific, that is, for the head, torso, or limbs. |
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