mlsanityData to 14 Oct 2026

IndoFashion : Apparel Classification for Indian Ethnic Clothes

arXiv 2104.02830 ↗First posted 6 Apr 2021cs.CV cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 13 references that are in the map. First posted on arXiv 6 Apr 2021.

Builds on

4 of 13 references.

References · 4

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2019DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Imagescited by 134
  4. 2019Atlas: A Dataset and Benchmark for E-commerce Clothing Product Categorizationcited by 4

Built on by

Not cited yet.