Comparison of class activation maps & gradient based class activation (GRAD-CAM) algorithm
This research project aims to investigate the heat map visualization techniques used for classifying images. The focus will be on Class Activation Mapping and Gradient Class Activation Mapping technique. The process includes implementation of the algorithms and proceed to do testing with different i...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/165964 |
| الوسوم: |
إضافة وسم
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| الملخص: | This research project aims to investigate the heat map visualization techniques used for classifying images. The focus will be on Class Activation Mapping and Gradient Class Activation Mapping technique. The process includes implementation of the algorithms and proceed to do testing with different images. The algorithm will be implemented using PyTorch and used on pre-trained models. The dataset used in the experiments were from the ImageNet. CAM uses global average pooling to generate a heatmap, while Grad-CAM uses gradients of the output class score with respect to the feature maps of the last convolutional layer to generate a more localized heatmap. |
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