November 15, 2019

581 words 3 mins read



UI visual interface for fastai - now compatible with Google Colab

repo name asvcode/Vision_UI
repo link
language Python
size (curr.) 26448 kB
stars (curr.) 213
created 2019-05-23


Visual UI interface for fastai

made-with-python GitHub license Open In Colab

Part1 Visual UI Demo Part1 Part2 Visual UI Demo Part2 Part3 Visual UI Demo Part2

Visual UI adds a graphical interface to fastai allowing the user to quickly load, choose parameters, train and view results without the need to dig deep into the code.



  • Update for compatability with fastai2
  • Files: Visual_UI2.ipyb and

Updates below are for version 1


  • Inclusion of ImageDataBunch.from_csv
  • Additional augmentations included [cutout, jitter, contrast, brightness, rotate, symmetric warp, padding]
  • Inclusion of ClassConfusion widget
  • Addition of ‘Code’ tab to view code


  • Under the ‘Info’ tab you can now easily upload some common datasets: Cats&Dogs, Imagenette, Imagewoof, Cifar and Mnist
  • Under the ‘Results’ tab if there are more than 2 classes the confusion matrix upgrades will not work but you can now view the confusion matrix

10/12/2019 - Open In Colab

09/25/2019 - xresnet architecture

  • xresnet architectures now working (using from fastai)

09/12/2019 - Confusion Matrix Upgrades (currently only works if there are 2 classes)

  • Under the Results tab, the confusion matrix tab now includes enhanced viewing features:

Option to view images with heatmaps or not

Option to view images within each section of the matrix

If heatmap option is ‘YES’ you can choose colormap, interpolation and alpha parameters

Examples of using different parameters for viewing images

Also have the option to view the images without the heatmap feature. Images within each matrix class display Index, Actual_Class, Predicted_Class, Prediction value, Loss and Image location

Images are stored within the path folder under their respective confusion matrix tags

View saved image files from various sections of the confusion matrix and compare their heatmap images.


  • after a training run, the model is saved in the models folder with the following name: ‘architecture’ + ‘pretrained’ + batchsize + image size eg: resnet50_pretrained_True_batch_32_image_128.pth
  • updated tkinter askdirectory code: now after choosing a file the tkinter dialogue box will be destroyed - previously the box would remain open


  • results tab added where you can load your saved model and plot multi_plot_losses, top_losses and Confusion_matrix


  • path and image_path (for augmentations) is now within vision_ui so no need to have a seperate cell to specify path
  • included link to fastai docs and forum in ‘info’ tab

All tabs are provided within an accordion design using ipywidgets, this allows for all aspects of choosing and viewing parameters in one line of sight

The Augmentation tab utilizes fastai parameters so you can view what different image augmentations look like and compare

View batch information

Review model data and choose suitable metrics for training

Review parameters get learning rate and train using the one cycle policy

Can experiment with various learning rates and train


  • fastai

I am using the developer version:

git clone

cd fastai


pip install -e ".[dev]"

for installation instructions visit Fastai Installation Readme

  • ipywidgets

pip install ipywidgets jupyter nbextension enable --py widgetsnbextension


conda install -c conda-forge ipywidgets

for installation instructions visit Installation docs

  • psutil

psutil (process and system utilities) is a cross-platform library for retrieving information on running processes and system utilization (CPU, memory, disks, network, sensors) in Python

pip install psutil


git clone this repository

git clone

run Visual_UI.ipynb and run display_ui()

Known Issues


Currently causing [display] issues with tkinter

Future Work

  • Integrate into fastai v2
comments powered by Disqus