CvT This is an official implementation of CvT: Introducing Convolutions to Vision Transformers. We present a new architecture, named Convolutional vision Transformers (CvT), that improves Vision Transformers (ViT) in performance and efficienty by introducing convolutions into ViT to yield the best of both disignes. This is accomplished through two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformer block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) […]

Rover Reverse engineer your CNNs, in style. Rover will help you break down your CNN and visualize the features from within the model. No need to write weirdly abstract code to visualize your model’s features anymore. :computer: Usage git clone https://github.com/Mayukhdeb/rover.git; cd rover install requirements: pip install -r requirements.txt from rover import core from rover.default_models import models_dict core.run(models_dict = models_dict) and then run the script with streamlit as: $ streamlit run your_script.py if everything goes right, you’ll see something like: […]