Going deeper into Deep CNNs through visualization methods: Saliency maps, optimize a random input image and deep dreaming with Keras
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Updated
May 17, 2020 - Jupyter Notebook
Going deeper into Deep CNNs through visualization methods: Saliency maps, optimize a random input image and deep dreaming with Keras
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.
First position in Gran Canary Datathon 2021
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.
Intracerebral Hemorrhage Detection on Computed Tomography Images Using a Residual Neural Network
PyTorch MobileNetV2 Stanford Cars Dataset Classification (0.85 Accuracy)
Prerocessing the images before classification as well as visualizations aiming at understanding how the final model performs classification
Code for the paper : "Weakly supervised segmentation with cross-modality equivariant constraints", available at https://arxiv.org/pdf/2104.02488.pdf
image classification using deep learning
Generate explanations for the ResNet50 classification using Grad-CAM and LIME (XAI Method)
Using LIME and Grad-CAM techniques to explain the results achieved by various image transfer learning techniques
Repository of the course project of CMU 16-824 Visual Learning and Recognition
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.
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
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!
Develop and train image classification models using advanced deep learning techniques to identify diseases specific to apples.
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.
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