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The research paper aims to compare various pretrained Cnn(Convolution neural network) models to analyze labelled dataset of cars to ascertain if it’s total loss or not. We aim to compare the various models in terms
of their accuracy, precision and various other benchmarks to decide what model can be used to best
determine if the damage is total loss or not.
The attached file contains more information about the dataset. The evaluation metrics should compare ( AlexNet, VGG16 and ResNet) the models mentioned in the attached file on the datasets mentioned. I will get the code and charts.
I would need the writer to get the abstract, methodologies, research already done in the field with reference and how does the current research aim to better the previous work or why is it different.
example of the format and structure expected –
https://pureadmin.qub.ac.uk/ws/portalfiles/portal/505842216/Vehicle_damage_severity_estimation.pdf#page=12&zoom=100,65,608
To assist you in creating the structure and content of the research paper, I’ll outline the key sections based on your requirements. Here’s a guideline for how to approach each section:
1. Abstract
- Purpose: State the goal of the paper, the problem being solved, and the evaluation of CNN models (AlexNet, VGG16, ResNet).
- Methodology: Mention that the research compares various pretrained CNN models on a labeled car dataset to predict total loss using metrics like accuracy and precision.
- Key Findings: Briefly mention the evaluation of these models and how they may perform in terms of benchmarks.
- Significance: Highlight why this research is important in assessing the potential for damage classification in cars.
Example:
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