  Cloth categorization is an important research problem that is used by
e-commerce websites for displaying correct products to the end-users. Indian
clothes have a large number of clothing categories both for men and women. The
traditional Indian clothes like "Saree" and "Dhoti" are worn very differently
from western clothes like t-shirts and jeans. Moreover, the style and patterns
of ethnic clothes have a very different distribution from western outfits. Thus
the models trained on standard cloth datasets fail miserably on ethnic outfits.
To address these challenges, we introduce the first large-scale ethnic dataset
of over 106k images with 15 different categories for fine-grained
classification of Indian ethnic clothes. We gathered a diverse dataset from a
large number of Indian e-commerce websites. We then evaluate several baselines
for the cloth classification task on our dataset. In the end, we obtain 88.43%
classification accuracy. We hope that our dataset would foster research in the
development of several algorithms such as cloth classification, landmark
detection, especially for ethnic clothes.
