Create README.md

README.md

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+---
+title: Continual learning results (%) on data separated by region groups
+license: mit  
+task_categories:
+- image-classification
+tags:
+- transfer-learning
+- resnet
+- vit
+- vit-hybrid        
+- swin-transformer  
+---
+
+# Continual Learning Results (Region Groups)
+
+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.
+
+
+---
+title: Continual learning results (%) on data separated by region groups
+license: mit  
+task_categories:
+- image-classification
+tags:
+- transfer-learning
+- resnet
+- vit
+- vit-hybrid        
+- swin-transformer  
+---
+
+# Continual Learning Results (Region Groups)
+
+這個儲存庫包含了基於多個主流視覺模型進行 Cross Entropy 方法微調（Fine-tuning）後的遷移學習（Transfer Learning）權重與實驗結果。
+
+## 📂 資料結構 (Directory Structure)
+
+```text
+.
+├── ResNet-34/
+│   ├── config.json              # 模型相關參數
+│   ├── model.safetensors        # 權重檔
+│   └── training_args.bin        # 訓練時所有超參數設定
+├── 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
+```
+
+## 🚀 如何載入指定方法的權重 (Usage)
+
+您可以使用 `huggingface_hub` SDK 來下載特定持續學習方法的權重檔案：
+
+```python
+from huggingface_hub import hf_hub_download
+
+# 範例：下載 Swin Transformer 的模型權重
+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"權重已成功下載至: {model_weight_path}")
+```
+
+## 🏗️ 基礎模型說明 (Base Models)
+
+本研究針對以下由 Hugging Face 提供的預訓練模型進行了連續學習微調：
+* **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`)
+
+## 📜 授權協議 (License)
+
+本微調模型衍生自上述開源架構，權重全面採用 **MIT** 協議釋出。您可以自由地在學術研究或商業項目中進行載入、修���與分發，惟須保留原作者之引用資訊。