DOI: 10.5281/zenodo.6670745
title: DECIMER Image classifier dataset

description: Images dataset divided into train (10905114 images), validation (2115528 images) and test (544946 images) folders containing a balanced number of images for two classes (chemical structures and non-chemical structures). The chemical structures were generated using RanDepict to random picked compounds from the ChEMBL30 database and the COCONUT database. The non-chemical structures were generated using Python or they were retrieved from several public datasets: COCO dataset, MIT Places-205 dataset, Visual Genome dataset, Google Open labeled Images, MMU-OCR-21 (kaggle), HandWritten_Character (kaggle), CoronaHack -Chest X-Ray-dataset (kaggle), PANDAS Augmented Images (kaggle), Bacterial_Colony (kaggle), Ceylon Epigraphy Periods (kaggle), Chinese Calligraphy Styles by Calligraphers (kaggle), Graphs Dataset (kaggle), Function_Graphs Polynomial (kaggle), sketches (kaggle), Person Face Sketches (kaggle), Art Pictograms (kaggle), Russian handwritten letters (kaggle), Handwritten Russian Letters (kaggle), Covid-19 Misinformation Tweets Labeled Dataset (kaggle) and grapheme-imgs-224x224 (kaggle). This data was used to build a CNN classification model using as a base model EfficienNetB0 and fine tuning it. The model is available on Github.
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