INTEL-landscape-classification

This project is an image classifier of Natural Scenes around the world.
This Data contains around 25k images of size 150x150 distributed under 6 categories.
These Categories include: 'buildings', 'forest', 'glacier', 'mountain', 'sea' and 'street'.
The Train, Test and Prediction data is separated in each zip files. There are around 14k images in Train, 3k in Test and 7k in Prediction.
The model has a straightforward CNN architecture with 4 convolutional layers followed by 2 fully connected layers.
The final testing precision is 89.47%.
You can find the dataset on Kaggle here.