sfarrukhm/intel-image-classification

Intel Image Classification

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

• Buildings

• Forest

• Glacier

• Mountain

• Sea

• Street

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📆 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.

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🧪 Structure

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

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🔗 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

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💻 Usage

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

from datasets import load_dataset

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