---
title: Transfer learning results (%)
license: mit  
task_categories:
- image-classification
tags:
- transfer-learning
- resnet
- vit
- vit-hybrid        
- swin-transformer  
---

# Transfer Learning Results

This repository contains the weights and experimental results of transfer learning fine-tuning using several mainstream vision backbones. The method was evaluated based on Cross Entropy.

## 📂 Directory Structure

```text
.
├── ResNet-34/
│   ├── config.json              # Model configuration parameters
│   ├── model.safetensors        # Model weights file
│   └── training_args.bin        # Training hyperparameters and arguments
├── Swin Transformer/
│   ├── config.json
│   ├── model.safetensors
│   └── training_args.bin
├── ViT/
│   ├── config.json
│   ├── model.safetensors
│   └── training_args.bin
└── ViT-H/
    ├── config.json
    ├── model.safetensors
    └── training_args.bin

```

## 🚀 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
REPO_ID = "Dataset-and-Model/transfer-learning"
config_path = hf_hub_download(
    repo_id=REPO_ID,
    filename="Swin Transformer/config.json",
    repo_type="model"
)
model_weight_path = hf_hub_download(
    repo_id=REPO_ID,
    filename="Swin Transformer/model.safetensors",
    repo_type="model"
)
training_args_path = hf_hub_download(
    repo_id=REPO_ID,
    filename="Swin Transformer/training_args.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.