---
title: Continual learning results (%) on data separated by region groups
license: mit  
task_categories:
- image-classification
tags:
- continual-learning
- resnet
- vit
- vit-hybrid        
- swin-transformer  
---

# Continual Learning Results (Region Groups)

This repository contains the weights and experimental results of continual learning fine-tuning using several mainstream vision backbones. The methods evaluated include FineTune (Cross Entropy), EWC, VCL, and FIP.

## 📂 Directory Structure

```text
.
├── FineTune_Cross_Entropy/
│   ├── model/
│   │   ├── config.json              # Model configuration and hyperparameters
│   │   └── pytorch_model.bin        # Model weights file
│   └── result/
│       ├── misclassified_images.csv # Records of misclassified images
│       ├── test_evaluation.csv      # Evaluation metrics and test results
│       ├── training_time.csv        # Logged training duration per group (hours)
│       └── training_metrics_set1.csv ~ training_metrics_set7.csv # Training dynamics and output logs
├── EWC/
│   ├── model/
│   │   ├── config.json
│   │   └── pytorch_model.bin
│   └── result/
│       ├── misclassified_images.csv
│       ├── test_evaluation.csv
│       ├── training_time.csv
│       └── training_metrics_set1.csv ~ training_metrics_set7.csv
├── VCL/
│   ├── model/
│   │   ├── config.json
│   │   └── pytorch_model.bin
│   └── result/
│       ├── misclassified_images.csv
│       ├── test_evaluation.csv
│       ├── training_time.csv
│       └── training_metrics_set1.csv ~ training_metrics_set7.csv
└── FIP/
    ├── model/
    │   ├── config.json
    │   └── pytorch_model.bin
    └── result/
        ├── misclassified_images.csv
        ├── test_evaluation.csv
        ├── training_time.csv
        └── training_metrics_set1.csv ~ training_metrics_set7.csv
```

## 🚀 Usage

You can download the model weights for a specific continual learning method using the `huggingface_hub` SDK:

```python
from huggingface_hub import hf_hub_download

# Example: Download Swin Transformer model weights for the VCL method
model_weight_path = hf_hub_download(
    repo_id="Dataset-and-Model/continual-learning-region-groups",
    filename="Swin Transformer/VCL/model/pytorch_model.bin",
    repo_type="model"
)

print(f"Weights successfully downloaded to: {model_weight_path}")
```

## 🏗️ Base Models

The fine-tuning and continual learning experiments in this study are built upon the following pre-trained models provided by Hugging Face:
* **ResNet-34** (e.g., `microsoft/resnet-34`)
* **Vision Transformer (ViT)** (e.g., `google/vit-large-patch32-384`)
* **ViT Hybrid** (e.g., `google/vit-hybrid-base-bit-384`)
* **SWIN Transformer** (e.g., `microsoft/swin-large-patch4-window12-384`)

## 📜 License

These fine-tuned model weights are derived from the open-source architectures mentioned above and are released under the **MIT License**. You are free to use, modify, and distribute these weights in both academic research and commercial applications, provided that proper attribution to the original authors is maintained.