Dataset-and-Model/continual-learning-taxonomic-groups

Continual Learning Results (Taxonomic 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

.
├── 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:

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-taxonomic-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:

• 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.