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iLearn-Lab/
ICMR25-HMKD

Name: ICMR25-HMKD
Availability: InStock

semantic-segmentation

knowledge-distillation

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

Mingzhu Xu¹ Jing Wang¹ Mingcai Wang¹ Yiping Li¹ Yupeng Hu^1✉ Xuemeng Song¹ Weili Guan¹

¹Affiliation (Please update if needed)

Official implementation of HMKD, a Heterogeneous Model Knowledge Distillation framework with Dual Alignment for Semantic Segmentation.

🔗 Conference: ICMR 2025
🔗 Task: Semantic Segmentation
🔗 Framework: PyTorch

📌 Model Information

1. Model Name

HMKD (Heterogeneous Model Knowledge Distillation)

2. Task Type & Applicable Tasks

Task Type: Semantic Segmentation / Model Compression
Core Task: Knowledge Distillation for segmentation
Applicable Scenarios:
- Lightweight model deployment
- Cross-architecture distillation
- Efficient semantic understanding

3. Project Introduction

Semantic segmentation models often rely on heavy architectures, limiting their deployment in resource-constrained environments. Knowledge distillation (KD) provides a promising solution by transferring knowledge from a large teacher model to a compact student model.

HMKD introduces a Dual Alignment Distillation Framework, which:

Aligns heterogeneous architectures between teacher and student models
Performs feature-level and prediction-level alignment
Bridges the representation gap across different model families
Improves segmentation accuracy while maintaining efficiency

4. Training Data Source

Supported datasets:

Cityscapes
CamVid

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

🚀 Environment Setup

Ubuntu 20.04.4 LTS
Python 3.8.10 (Anaconda recommended)
CUDA 11.3
PyTorch 1.11.0
NCCL 2.10.3

Install dependencies:

bash

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

⚙️ Pre-trained Weights

Initialization Weights

ResNet-18
ResNet-101
SegFormer-B0
SegFormer-B4

(Download from official PyTorch and Google Drive links)

Trained Weights

Download trained HMKD models:

Baidu Cloud: https://pan.baidu.com/s/1xw_6ts5VNV73vXeOLAokwQ?pwd=jvx8

🚀 Training

Download datasets and pre-trained weights
Generate dataset path lists (.txt files)
Update dataset paths in the code

Run training: