🚀 HMKD-ICMR: Heterogeneous Model Knowledge Distillation via Dual Alignment for Semantic Segmentation
Mingzhu Xu1
Jing Wang1
Mingcai Wang1
Yiping Li1
Yupeng Hu1✉
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
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## 📌 Model Information
### 1. Model Name
**HMKD** (Heterogeneous Model Knowledge Distillation)
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### 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
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### 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
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### 4. Training Data Source
Supported datasets:
- **Cityscapes**
- **CamVid**
| Dataset | Train | Val | Test | Classes |
|--------|------|-----|------|--------|
| Cityscapes | 2975 | 500 | 1525 | 19 |
| CamVid | 367 | 101 | 233 | 11 |
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## 🚀 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
```
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## ⚙️ Pre-trained Weights
### Initialization Weights
- ResNet-18
- ResNet-101
- SegFormer-B0
- SegFormer-B4
(Download from official PyTorch and Google Drive links)
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### Trained Weights
Download trained HMKD models:
- Baidu Cloud: https://pan.baidu.com/s/1xw_6ts5VNV73vXeOLAokwQ?pwd=jvx8
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## 🚀 Training
1. Download datasets and pre-trained weights
2. Generate dataset path lists (.txt files)
3. Update dataset paths in the code
### Run training:
```bash
CUDA_VISIBLE_DEVICES=0,1 nohup python -m torch.distributed.launch --nproc_per_node=2 train_NEW_AEU_kd.py > train.log 2>&1 &
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 train_NEW_AEU_kd.py
```
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## ⚠️ Notes
- Designed for research purposes
- Performance depends on teacher-student architecture pairing
- Multi-GPU training is recommended
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## 📝 Citation
```bibtex
@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}
}
```
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## 📬 Contact
For questions or collaboration, please contact the corresponding author.
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