Update README.md

README.md

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 license: mit
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-# METHODNAME: Hierarchical Contrastive Learning for Patent Image Retrieval
+# PHOENIX: Hierarchical Contrastive Learning for Patent Image Retrieval
 
-**METHODNAME** is a domain-adapted CLIP/ViT-based model designed to improve **patent image retrieval**. It addresses the unique challenges of retrieving relevant technical drawings in patent documents, especially when searching for **semantically or hierarchically related images**, not just exact matches.
+**PHOENIX** is a domain-adapted CLIP/ViT-based model designed to improve **patent image retrieval**. It addresses the unique challenges of retrieving relevant technical drawings in patent documents, especially when searching for **semantically or hierarchically related images**, not just exact matches.
 
 This model is based on `openai/clip-vit-base-patch16` and fine-tuned using a **hierarchical multi-positive contrastive loss** that leverages **Locarno classification** — an international system used to categorize industrial designs.
 
@@ -72,7 +72,7 @@ You can now compare cosine similarity between embeddings to retrieve similar pat
 
 ## 🏆 Results
 
-Evaluated on the **DeepPatent2** dataset, METHODNAME shows significant gains in:
+Evaluated on the **DeepPatent2** dataset, PHOENIX shows significant gains in:
 - **Intra-category retrieval** (same subclass)
 - **Cross-category generalization** (related but distinct inventions)
 - **Low-parameter robustness**, making it suitable for real-time deployment