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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.
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