grad-cam-visualization
There are 31 repositories under grad-cam-visualization topic.
ashutosh1919/explainable-cnn
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
IbrahimSobh/see-inside-cnn
Going deeper into Deep CNNs through visualization methods: Saliency maps, optimize a random input image and deep dreaming with Keras
gaurav104/WSS-CMER
Code for the paper : "Weakly supervised segmentation with cross-modality equivariant constraints", available at https://arxiv.org/pdf/2104.02488.pdf
Arka-Bhowmik/mri_triage_normal
Deep Learning Breast MRI Segmentation and Classification
WaterKnight1998/spegc-datathon
First position in Gran Canary Datathon 2021
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.
haksorus/mobilenetv2-cars-classification
PyTorch MobileNetV2 Stanford Cars Dataset Classification (0.85 Accuracy)
manjaryp/GANvsGraphicsvsReal
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet
PerXeptron/CAM-Localization
Heat Map :fire: Generation codes for using PyTorch and CAM Localization Algorithm.
miguelaltuve/ICHdetection
Intracerebral Hemorrhage Detection on Computed Tomography Images Using a Residual Neural Network
MK-ek11/Apply-XAI-on-ResNet50
Generate explanations for the ResNet50 classification using Grad-CAM and LIME (XAI Method)
shantanu-ai/Explainability-with-LTH
Repository of the course project of CMU 16-824 Visual Learning and Recognition
AhmedBALAAZI/Detection-and-localization-of-COVID-19-and-Pneumonia-on-chest-radiographs
Detection and localization of COVID-19 on chest X-rays
Lusanji/Explainable-car-brand-classification-
Using LIME and Grad-CAM techniques to explain the results achieved by various image transfer learning techniques
muyishen2040/Racing-AI-Reinforcement-Learning-for-Mario-Kart-64
Fork of the Mario Kart 64 Gym Environment. Includes training scripts for RL algorithms and Grad-CAM visualization
Safaa-p/Disease-classification-Plant-Village-dataset
Develop and train image classification models using advanced deep learning techniques to identify diseases specific to apples.
behzadshomali/Grad_CAM_Pytorch
Have you ever asked yourself, which regions of the input image were considered more by the model? If so, Grad-CAM has exciting answers for you!
coderjolly/chest-x-ray-classification
This study tries to compare the detection of lung diseases using xray scans from three different datasets using three different neural network architectures using Pytorch and perform an ablation study by changing learning rates. The dimensional understanding is visualised using t-SNE and Grad-CAM for visualisation of diseases in x-ray scans.
d4nh5u/Covid19_CT_Attention
Exploring the Application of Attention Mechanisms in Conjunction with Baseline Models on the COVID-19-CT Dataset
Neloy-Barman/Interpretable-Bengali-Fish-Recognizer
Collecting fish image data, after training classifiers grad-cam is applied for the prediction interpretation
rae-t627/KL-Severity-Grading-Focal-Loss-Optimization-Grad-CAM
KL severity grading using SE-ResNet and SE-DenseNet architectures trained with Cross Entropy loss and Focal Loss. The hyperparameters of focal loss have been fine-tuned as well. Further, Grad-CAM has been implemented for visualization purposes.
TyBruceChen/Grad-CAM-pytorch---Understand-deep-learning-from-higher-view
Gradient Class Activation Map (with pytorch): Visualize the model's prediction to help understand CNN and ViT models better
Wenhao-Yang/GradientFrequencyAttention
Gradient Frequency Attention: Tell Neural Networks where speaker information is.
AhmadRK94/Image-Classification-PyTorch
image classification using deep learning
anoopkdcs/GANvsGraphicsvsReal
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet
IDU-CVLab/Images_Preprocessing
Prerocessing the images before classification as well as visualizations aiming at understanding how the final model performs classification
matteo-giri/bachelor-degree-thesis
Thesis name: Comparative evaluation of deep neural networks for automatic detection of violence scenes within videos
NeelBhowmik/imfication
Image classification using deep learning models with activation map visualisation and TensorRT support
usmarcv/deele-rad
DEELE-Rad: Deep Learning-based Radiomics
cm-awais/sar_performance_metrics
Deep Learning for SAR Ship classification: Focus on Unbalanced Datasets and Inter-Dataset Generalization
ZizZu94/resnet-grad-cam
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.