Folders and files
| Name | Name | Last commit date | ||
|---|---|---|---|---|
Graphical User interface for fastai
currently only works with version 1
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.
- Can now be pip installed fast-gui
- Update for compatability with fastai2
- Files: Visual_UI2.ipyb and vision_ui2.py
Files: Visual_UI.ipyb and vision_ui.py
- 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
- Works with Google Colab (https://github.com/asvcode/Colab_UI) - Results tab not currently available in Colab
- xresnet architectures now working (using xresnet2.py from fastai)
- 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.
False Positive
True Positive
True Negative
False Negative
- 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 https://github.com/fastai/fastai
cd fastai
tools/run-after-git-clone
pip install -e ".[dev]"
for installation instructions visit Fastai Installation Readme
- ipywidgets
pip install ipywidgets jupyter nbextension enable --py widgetsnbextension
or
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 https://github.com/asvcode/Vision_UI.git
run Visual_UI.ipynb and run display_ui()
- Colab - version 1 works with colab Colab_UI but is glitchy
- Integrate into fastai v2 - Compatability with fastai v2 done but not with the full functionality as the v1 version
- Create pip install verson - Done! fast-gui
- Include full functionality with v2 version
- Create a v2 that is compatible with Colab