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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.
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