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Heatmap Visualization of a Image Classification Model like Xception using GRAD-CAM #880
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Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊 |
Nice approach. Issue assigned to you @AMS003010. Make sure you complete this issue within the deadline of August 10th, 2024. |
@abhisheks008 |
🔴 Request for Assignment: I would like to request assignment for the issue titled "Heatmap Visualization of an Image Classification Model like Xception using GRAD-CAM". For this project, I plan to use the following models(using pre-trained weights):
I will be implementing the Grad-CAM technique to visualize the regions in images that are important for the model's decision-making process. The goal is to generate heatmaps that help explain why the model classifies an image in a certain way. 🔴 Participant Details:
Looking forward to contributing to this project! |
Hi @ParasSethi737 go ahead. Issue assigned to you! |
@abhisheks008 |
Hello @ParasSethi737! Your issue #880 has been closed. Thank you for your contribution! |
Deep Learning Simplified Repository (Prop.osing new issue)
🔴 Project Title : Heatmap Visualization of a Image Classification Model like Xception using GRAD-CAM
🔴 Aim : GRAD-CAM, which stands for Gradient-weighted Class Activation Mapping, is a technique used in the field of computer vision to visualize the regions of an image that are important for a convolutional neural network's decision-making process.So I would like to use Deep learning techniques like GRAD-CAM to explain why the Xception model is classifying that as an "Persian cat" ( Or anything else ) visually through a heatmap.
🔴 Dataset : Not applicable as I will be using an Xception model with the
imagenet
weights to explain the reson it classified that as that using GRAD CAM🔴 Approach : Since ML techniques like CNN are essentially "Black Boxes", it is hard for us to understand why it made that choice. Using GRAD-CAM we are able to explain why the CNN model made that particular choice that it did. It helps us to visually understand the "why" of the classification.
📍 Follow the Guidelines to Contribute in the Project :
requirements.txt
- This file will contain the required packages/libraries to run the project in other machines.Model
folder, theREADME.md
file must be filled up properly, with proper visualizations and conclusions.🔴🟡 Points to Note :
✅ To be Mentioned while taking the issue :
Happy Contributing 🚀
All the best. Enjoy your open source journey ahead. 😎
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