📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
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Updated
Aug 29, 2023 - Python
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
Going deeper into Deep CNNs through visualization methods: Saliency maps, optimize a random input image and deep dreaming with Keras
Code for the paper : "Weakly supervised segmentation with cross-modality equivariant constraints", available at https://arxiv.org/pdf/2104.02488.pdf
First position in Gran Canary Datathon 2021
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet
Intracerebral Hemorrhage Detection on Computed Tomography Images Using a Residual Neural Network
Heat Map 🔥 Generation codes for using PyTorch and CAM Localization Algorithm.
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.
Deep Learning Breast MRI Segmentation and Classification
Prerocessing the images before classification as well as visualizations aiming at understanding how the final model performs classification
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet
Exploring the Application of Attention Mechanisms in Conjunction with Baseline Models on the COVID-19-CT Dataset
Collecting fish image data, after training classifiers grad-cam is applied for the prediction interpretation
image classification using deep learning
Gradient Frequency Attention: Tell Neural Networks where speaker information is.
rad-Cam provides us with a way to look into what particular parts of the image influenced the whole model’s decision for a specifically assigned label. It is particularly useful in analyzing wrongly classified samples.
Develop and train image classification models using advanced deep learning techniques to identify diseases specific to apples.
Detection and localization of COVID-19 on chest X-rays
Using LIME and Grad-CAM techniques to explain the results achieved by various image transfer learning techniques
Image classification using deep learning models with activation map visualisation and TensorRT support
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