# hYOLO Model: Enhancing Object Classification with Hierarchical Context in YOLOv8

arXiv: https://arxiv.org/abs/2510.23278 · HTML: https://mlsanity.com/paper/2510.23278/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 21 of 21)

- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://mlsanity.com/paper/2006.04388/) (2020)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks](https://mlsanity.com/paper/1912.09393/) (2019)
- [Evaluation Measures for Hierarchical Classification: a unified view and novel approaches](https://mlsanity.com/paper/1306.6802/) (2013)
- [What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector](https://mlsanity.com/paper/2408.15857/) (2024)
- [B-CNN: Branch Convolutional Neural Network for Hierarchical Classification](https://mlsanity.com/paper/1709.09890/) (2017)
- [Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning](https://mlsanity.com/paper/1802.05800/) (2018)
- [HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification](https://mlsanity.com/paper/2209.10218/) (2022)
- [Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation](https://mlsanity.com/paper/2408.04804/) (2024)
- [Skin Lesion Recognition with Class-Hierarchy Regularized Hyperbolic Embeddings](https://mlsanity.com/paper/2209.05842/) (2022)
- [Analysis and Comparison of Classification Metrics](https://mlsanity.com/paper/2209.05355/) (2022)
- [Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies](https://mlsanity.com/paper/2009.05609/) (2020)
- [Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study](https://mlsanity.com/paper/2010.04049/) (2020)
- [Learn Class Hierarchy using Convolutional Neural Networks](https://mlsanity.com/paper/2005.08622/) (2020)
- [Hierarchical confusion matrix for classification performance evaluation](https://mlsanity.com/paper/2306.09461/) (2023)
- [Hierarchical Class-Based Curriculum Loss](https://mlsanity.com/paper/2006.03629/) (2020)
- [A Novel Hierarchical-Classification-Block Based Convolutional Neural Network for Source Camera Model Identification](https://mlsanity.com/paper/2212.04161/) (2022)
- [Hierarchical Object Detection and Recognition Framework for Practical Plant Disease Diagnosis](https://mlsanity.com/paper/2407.17906/) (2024)
- [HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection](https://mlsanity.com/paper/2503.07371/) (2025)

## Abstract (arXiv)

Current convolution neural network (CNN) classification methods are predominantly focused on flat classification which aims solely to identify a specified object within an image. However, real-world objects often possess a natural hierarchical organization that can significantly help classification tasks. Capturing the presence of relations between objects enables better contextual understanding as well as control over the severity of mistakes. Considering these aspects, this paper proposes an end-to-end hierarchical model for image detection and classification built upon the YOLO model family. A novel hierarchical architecture, a modified loss function, and a performance metric tailored to the hierarchical nature of the model are introduced. The proposed model is trained and evaluated on two different hierarchical categorizations of the same dataset: a systematic categorization that disregards visual similarities between objects and a categorization accounting for common visual characteristics across classes. The results illustrate how the suggested methodology addresses the inherent hierarchical structure present in real-world objects, which conventional flat classification algorithms often overlook.