ICMR25-HMKD

HMKD: Heterogeneous Model Knowledge Distillation via Dual Alignment for Semantic Segmentation

Official PyTorch implementation of the ICMR 2025 paper “Heterogeneous Model Knowledge Distillation via Dual Alignment for Semantic Segmentation”.

Authors

Mingzhu Xu¹, Jing Wang¹, Mingcai Wang¹, Yiping Li¹, Yupeng Hu¹, Xuemeng Song²*, Weili Guan³

¹ Shandong University
² City University of Hong Kong
³ Harbin Institute of Technology (Shen Zhen)
* Corresponding author

Introduction

This project is the official implementation of the paper “Heterogeneous Model Knowledge Distillation via Dual Alignment for Semantic Segmentation”.

HMKD proposes a heterogeneous model knowledge distillation framework for semantic segmentation, which effectively addresses the challenge of knowledge transfer caused by structural differences between teacher and student networks through a dual alignment mechanism:

Core Idea：Utilize dual alignment mechanisms to perform knowledge alignment simultaneously in both feature space and logit space.
Distillation Scenario：Supports knowledge transfer from large heterogeneous models (e.g., SegFormer) to lightweight models (e.g., DeepLabV3-ResNet18).
This Repository Provides：Complete distillation training code, pretrained weight interfaces for multiple backbone networks, and evaluation pipelines on mainstream segmentation datasets.

Example Description

We present HMKD, a framework for Semantic Segmentation via Knowledge Distillation.
Our method addresses architectural heterogeneity between teacher and student models by introducing dual alignment mechanisms.
This repository provides the official implementation, distilled checkpoints, and evaluation scripts.

Highlights

Supports heterogeneous model distillation (e.g., Transformer to CNN).
Proposes a Dual Alignment mechanism that significantly improv
Achieves strong performance on standard datasets such as Cityscapes and CamVid.

Project Structure

.
├── configs/               # Experiment configuration files
├── data/                  # Dataset paths and preprocessing scripts
├── models/                # Implementations of teacher and student networks
├── train_NEW_AEU_kd.py    # Core distillation training script
├── README.md
└── requirements.txt       # Environment dependencies

Installation

1. System Requirements

Ubuntu 20.04.4 LTS
Python 3.8.10 (Recommended: Anaconda)
CUDA 11.3 / PyTorch 1.11.0 / NCCL 2.10.3

2. Clone the repository

git clone [https://github.com/iLearn-Lab/HMKD-ICMR.git](https://github.com/iLearn-Lab/HMKD-ICMR.git)
cd HMKD-ICMR

3. Install Python packages

pip install timm==0.3.2 mmcv-full==1.2.7 opencv-python==4.5.1.48

Checkpoints / Models

1. Initialization Weights (for Training)

Please download the following pretrained weights according to your experimental needs:

DeepLabV3 - ResNet-18: resnet18.pth
DeepLabV3 - ResNet-101: resnet101.pth
Segformer - mit-b0: segformerb0.pth
Segformer - mit-b4: segformerb4.pth

2. Trained HMKD Weights (for Testing)

Download: Baidu Drive (Password: jvx8)

Dataset / Benchmark

Dataset	Train Size	Val Size	Test Size	Classes
Cityscapes	2975	500	1525	19
CamVid	367	101	233	11

lease generate the corresponding dataset path list files (.txt) in the code.

Usage

Training

After downloading the pretrained weights and datasets, launch the distillation task using distributed training:

# training mode
CUDA_VISIBLE_DEVICES=0,1 nohup python -m torch.distributed.launch --nproc_per_node=2 train_NEW_AEU_kd.py > train_distill.log 2>&1 &

# debugging mode
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 train_NEW_AEU_kd.py

Testing

Download the distilled weights, modify the path variable in the code, and run:

python evaluate.py --model_id HMKD --dataset cityscapes

Citation

If you use this code or method in your research, please cite our paper:

@ARTICLE{HMKD,
  author={Xu, Mingzhu and Wang, Jing and Wang, Mingcai and Li, Yiping and Hu, Yupeng and Song, Xuemeng and Guan, Weili},
  journal={ICMR}, 
  title={Heterogeneous Model Knowledge Distillation via Dual Alignment for Semantic Segmentation}, 
  year={2025}
}

License

This project is released under the Apache License 2.0.