Break down your CNN and visualize the features from within the model
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:
You can now view your Streamlit app in your browser.
Local URL: http://localhost:8501
:mage: Custom models
rover supports pretty much any PyTorch model with an input of shape [N, 3, H, W] (even segmentation models/VAEs and all that fancy stuff) with