Update README.md

README.md

@@ -77,83 +77,3 @@ The fine-tuning and continual learning experiments in this study are built upon
 ## 📜 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、修改與分發，惟須保留原作者之引用資訊。