deep-learning-imagenet

Comparison of different DL models used on ImageNet database.

1. Create virtual environment for Python

python -m venv deep-learning-imagenet-virtenv

2. Activate virtual environment for Python

deep-learning-imagenet-virtenv/Scripts/activate.bat

3. Upgrade pip

python -m pip install --upgrade pip

4. Install required libraries

pip install -r deep-learning-imagenet/requirements.txt

5. Create a Jupyter notebook specific to deep-learning-imagenet-virtenv

ipython kernel install --user --name=deep-learning-imagenet-virtenv

6. Start Application

python wsgi.py

7. Deactivate virtual environment

deactivate

8. Visualization URL

localhost 

References

1. Dataset: https://patrykchrabaszcz.github.io/Imagenet32/
2. ImageNet labels: https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a
3. A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets (Patryk Chrabaszcz, Ilya Loshchilov & Frank Hutter, 2017)