Train and predict your model on pre-trained deep learning models through the GUI (web app). No more many parameters, no more data preprocessing.
My goal is to simplify the installation and training of pre-trained deep learning models through the GUI (or you can call web app) without writing extra code. Set your dataset and start the training right away and monitor it with TensorBoard or DLTGUI tool. No more many parameters, no more data preprocessing.
While developing this application, I was inspired by the DIGITS system developed by NVIDIA.
In the words of Stephen Hawking:
Science is beautiful when it makes simple explanations of phenomena or connections between different observations. Examples include the double helix in biology and the fundamental equations of physics.
Guide - Youtube Video (Coming Soon)
Many bugs have been solved.
You will be able to Fine-Tuning your model. In this way, you can easily increase the success rate of the model.
You will be able to see which parts your model focuses on while classifying images (Class activation map, heat map - heatmap - available for MobileNetV2 only)
The following is an example of how a dataset should be structured. Before you train a deep learning model, put all your dataset into datasets directory.
├──datasets/
├──example_dataset/
├── cat
│ ├── img_1.jpg/png
│ └── img_2.jpg/png
├──flower_photos/
├── daisy
│── dandelion
│── roses
│── sunflowers
│── tulips
For image classification.
cd Deep-Learning-Training-GUI
pip install -r requirements.txt
python app.py
. You can access the program on localhost:5000
flower_photos
folder in the datasets and I will write to the form element like this: datasets/flower_photos
flower_photos
folder. This is our class count. When you train your own data set, you have to create as many folders here as you have classes.When you start to training, you will be able to access TensorBoard without writing any script on terminal!
Check localhost:6006
Contributions with example scripts for other frameworks (PyTorch or Caffe 2) and other pre-trained models are welcome!
Coming soon.
Font Awesome [4]
Boostrap V4 [5]
How to Easily Deploy Machine Learning Models Using Flask [6]
Simple and efficient data augmentations using the Tensorfow tf.Data and Dataset API [8]
Marcus D Bloice, Peter M Roth, Andreas Holzinger, Biomedical image augmentation using Augmentor, Bioinformatics, https://doi.org/10.1093/bioinformatics/btz259 [9]