pytorch-grad-cam and Explainable-AI-Scene-Classification-and-GradCam-Visualization
About pytorch-grad-cam
jacobgil/pytorch-grad-cam
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
This helps data scientists, machine learning engineers, and researchers understand why their computer vision AI models make specific decisions. You input a trained image classification, object detection, or segmentation model, and it outputs visual heatmaps showing the exact regions of an image that influenced the model's prediction. This allows users to diagnose model errors, build trust in AI systems, and improve model performance.
About Explainable-AI-Scene-Classification-and-GradCam-Visualization
baotramduong/Explainable-AI-Scene-Classification-and-GradCam-Visualization
We will build and train a Deep Convolutional Neural Network (CNN) with Residual Blocks to detect the type of scenery in an image. In addition, we will also use a technique known as Gradient-Weighted Class Activation Mapping (Grad-CAM) to visualize the regions of the inputs and help us explain how our CNN models think and make decision.
This project helps you automatically categorize images by the type of scene they depict, such as a forest, beach, or city. It takes an image as input and outputs the predicted scene category. Additionally, it visualizes the specific areas of the image that led to that classification, helping you understand why the AI made its decision. This is useful for anyone working with large collections of images who needs to organize them or verify an automated classification.
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