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Math symbols image dataset preview

Math symbols Image Classification Dataset

3.4K labeled images
Free download
3.4K+ labeled math symbols images ready for AI and computer vision research. Delivered as an ImageFolder-style ZIP with train / val / test splits and class-labeled folders. Review the source license before use. Compatible with PyTorch, TensorFlow, Keras, and any classification pipeline.

Download dataset · 3.4K imagesSupport the project

Browse a selection of samples from the Math symbols image dataset.

Sample 1 – Math symbols
Sample 2 – Math symbols
Sample 3 – Math symbols
Sample 4 – Math symbols
Sample 5 – Math symbols
Sample 6 – Math symbols
Sample 7 – Math symbols
Sample 8 – Math symbols
Sample 9 – Math symbols
Sample 10 – Math symbols
Sample 11 – Math symbols
Sample 12 – Math symbols
Sample 13 – Math symbols
Sample 14 – Math symbols
Sample 15 – Math symbols
Sample 16 – Math symbols
Sample 17 – Math symbols
Sample 18 – Math symbols
Sample 19 – Math symbols
Sample 20 – Math symbols
Sample 21 – Math symbols
Sample 22 – Math symbols
Sample 23 – Math symbols
Sample 24 – Math symbols

Categories in this dataset

EquivalentMultiplicationAdditionDecimal pointSubtractionDivision

Tags

Math symbols labeled image dataset
Math symbols photos
Math symbols pictures
Math symbols images
Math symbols dataset
Math symbols image dataset

Data sources

What you get

Math symbols classification dataset, ready to train

Clean folder layout

`train` / `val` / `test` sub-folders per class plus a `meta.json` with label list, split counts, and ImageNet normalization constants — ready for `torchvision.datasets.ImageFolder`.


Your choices, your ZIP

Pick output size, color mode, train/val/test split, and 20 augmentations. The ZIP is built for your exact setup.


Research-friendly

BibTeX citation per dataset and a Colab starter notebook. No signup required to download.


Framework-agnostic

PyTorch, TensorFlow, Ultralytics — pick any. Standard ImageFolder layout works out of the box.

Ready to train?

Configure the split and augmentations, then grab the ZIP.

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