metadata

license: other
task_categories:
  - image-classification
language:
  - en
pretty_name: Intel Image Classification
size_categories:
  - 10K<n<100K

Intel Image Classification

The Intel Image Classification dataset contains images of natural scenes categorized into six classes:

Buildings
Forest
Glacier
Mountain
Sea
Street

📆 Content

The dataset contains ~25,000 images of size 150x150 pixels.

Images are evenly distributed across 6 categories:

{'buildings' -> 0,
 'forest'    -> 1,
 'glacier'   -> 2,
 'mountain'  -> 3,
 'sea'       -> 4,
 'street'    -> 5 }

It is divided into three parts:
- Training set: ~14,000 images
- Test set: ~3,000 images
- Prediction set: ~7,000 images

The train, test, and prediction images are stored in separate folders.

🧪 Structure

data/
├── seg_train/
│   ├── buildings/
│   ├── forest/
│   ├── glacier/
│   ├── mountain/
│   ├── sea/
│   └── street/
├── seg_test/
│   └── ...
└── seg_pred/
    └── ...

🔗 Source & Acknowledgements

Originally published by Intel as part of a challenge on Analytics Vidhya:
https://datahack.analyticsvidhya.com
Rehosted on Kaggle:
Intel Image Classification | Kaggle

💻 Usage

You can load this dataset using Hugging Face's datasets library:

from datasets import load_dataset

dataset = load_dataset("sfarrukhm/intel-image-classification")