This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images
and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a
"fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs).
Here is the list of classes in the CIFAR-100:
Usage of Streamlit
streamlit run stream.py
The result is as follows:
Datasets is trained with tensorflow API. GPU is recommended for this datasets. Because it takes a lot of time on cpu.
Models in google drive.
- we have deep neural network with keras in tensorflow
Install python and:
pip install -r requirements.txt