| --- |
| 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. |
|
|
