Problem Statement

How do we, humans, recognize a forest as a forest or a mountain as a mountain? We are very good at categorizing scenes based on the semantic representation and object affinity, but we know very little about the processing and encoding of natural scene categories in the human brain. In this problem, you are provided with a dataset of ~25k images from a wide range of natural scenes from all around the world. Your task is to identify which kind of scene can the image be categorized into.

Note - This dataset can be found on kaggle

Dateset Details

Created by Intel for an image classification contest, this expansive image dataset contains approximately 25,000 images. Furthermore, the images are divided into the following categories: buildings, forest, glacier, mountain, sea, and street. The dataset has been divided into folders for training, testing, and prediction. The training folder includes around 14,000 images and the testing folder has around 3,000 images. Finally, the prediction folder includes around 7,000 images.

import os
import torch
import torchvision
import tarfile
from torchvision.datasets.utils import download_url
from torch.utils.data import random_split

project_name='Intel-Image_classification'

# Upload kaggle.jason
# please follow this link incase not aware: https://www.kaggle.com/general/74235
from google.colab import files
files.upload()

Saving kaggle.json to kaggle.json

{'kaggle.json': b'{"username":"hargurjeet","key":"c3882bdbb49388021171402c7018655e"}'}