Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+tags:
+- semantic-segmentation
+- knowledge-distillation
+- multimodal
+- model-compression
+- pytorch
+---
+
+<a id="top"></a>
+<div align="center">
+  <h1>🚀 HMKD-ICMR: Heterogeneous Model Knowledge Distillation via Dual Alignment for Semantic Segmentation</h1>
+
+  <p>
+    <b>Mingzhu Xu</b><sup>1</sup>&nbsp;
+    <b>Jing Wang</b><sup>1</sup>&nbsp;
+    <b>Mingcai Wang</b><sup>1</sup>&nbsp;
+    <b>Yiping Li</b><sup>1</sup>&nbsp;
+    <b>Yupeng Hu</b><sup>1✉</sup>&nbsp;
+    <b>Xuemeng Song</b><sup>1</sup>&nbsp;
+    <b>Weili Guan</b><sup>1</sup>
+  </p>
+
+  <p>
+    <sup>1</sup>Affiliation (Please update if needed)
+  </p>
+</div>
+
+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
+
+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
+```
+
+---
+
+## ⚠️ Notes
+
+- Designed for research purposes  
+- Performance depends on teacher-student architecture pairing  
+- Multi-GPU training is recommended  
+
+---
+
+## 📝 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}
+}
+```
+
+---
+
+## 📬 Contact
+
+For questions or collaboration, please contact the corresponding author.
+
+---