update README and norm mean

README.md

@@ -6,7 +6,29 @@ tags:
 - medical
 ---
 
-# MONET (Medical cONcept rETrieve)
+# MONET (Medical cONcept rETriever)
+
+## Description
+
+[MONET]() is a CLIP ViT-L/14 vision-language foundation model trained on 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, competitively with
+supervised models built on previously concept-annotated dermatology datasets of clinical images. MONET enables AI transparency across the entire AI system development pipeline from building inherently interpretable models to dataset and model auditing.
+
+* [GitHub](https://github.com/suinleelab/MONET)
+* [Paper](https://github.com/suinleelab/MONET)
+
+## Citation
+
+```bibtex
+@article{kim2024transparent,
+    title={Transparent medical image AI via an image–text foundation model grounded in
+medical literature},
+    author={Chanwoo Kim and Soham U. Gadgil and Alex J. DeGrave and Jesutofunmi A. Omiye and Zhuo Ran Cai and Roxana Daneshjou and Su-In Lee},
+    journal={Nature Medicine},
+    #pages={1--10},
+    year={2024},
+    publisher={Nature Publishing Group US New York}
+}
+```
 
 Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
 

preprocessor_config.json

@@ -5,15 +5,15 @@
   "do_resize": true,
   "feature_extractor_type": "CLIPFeatureExtractor",
   "image_mean": [
-    0.48145466,
-    0.4578275,
-    0.40821073
+    0.485,
+    0.456,
+    0.406
   ],
   "image_std": [
-    0.26862954,
-    0.26130258,
-    0.27577711
+    0.229,
+    0.224,
+    0.225
   ],
   "resample": 3,
   "size": 224
-}
+}