# dlordinal: a Python package for deep ordinal classification

arXiv: https://arxiv.org/abs/2407.17163 · HTML: https://mlsanity.com/paper/2407.17163/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels](https://mlsanity.com/paper/1501.06202/) (2015)
- [Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation](https://mlsanity.com/paper/1703.10757/) (2017)
- [A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis](https://mlsanity.com/paper/1905.09113/) (2019)
- [Unimodal Distributions for Ordinal Regression](https://mlsanity.com/paper/2303.04547/) (2023)
- [A Dictionary-based approach to Time Series Ordinal Classification](https://mlsanity.com/paper/2305.09288/) (2023)

## Built on by (most cited first, 4 of 4)

- [TOC-UCO: a comprehensive repository of tabular ordinal classification datasets](https://mlsanity.com/paper/2507.17348/) (2025)
- [Enhancing LLM-Based Data Annotation with Error Decomposition](https://mlsanity.com/paper/2601.11920/) (2026)
- [Reliable Conformal Prediction for Ordinal Classification Using the Ranked Probability Score](https://mlsanity.com/paper/2606.24959/) (2026)
- [From Kellgren-Lawrence to Calcium Pyrophosphate Crystal Deposition: A Soft-Labelling Framework for Knee Osteoarthritis Assessmen](https://mlsanity.com/paper/2605.28176/) (2026)

## Abstract (arXiv)

dlordinal is a new Python library that unifies many recent deep ordinal classification methodologies available in the literature. Developed using PyTorch as underlying framework, it implements the top performing state-of-the-art deep learning techniques for ordinal classification problems. Ordinal approaches are designed to leverage the ordering information present in the target variable. Specifically, it includes loss functions, various output layers, dropout techniques, soft labelling methodologies, and other classification strategies, all of which are appropriately designed to incorporate the ordinal information. Furthermore, as the performance metrics to assess novel proposals in ordinal classification depend on the distance between target and predicted classes in the ordinal scale, suitable ordinal evaluation metrics are also included. dlordinal is distributed under the BSD-3-Clause license and is available at https://github.com/ayrna/dlordinal.