Add Kermany dataset, reported result and validated training instructions

README.md

@@ -17,9 +17,9 @@ base_model: timm/vit_base_patch16_224.augreg_in21k_ft_in1k
 
 **Sequential Hybrid Vision Transformer with MedMamba for Retinal Disease Classification**
 
-Accepted for presentation at **IEEE GSCon 2027**
+Accepted for presentation at **IEEE GSCon 2027**.
 
-Khamir Desai · Sree Saicharan Vadapalli · Mayuri A. Mehta 
+Khamir Desai · Mayuri A. Mehta · Sree Saicharan Vadapalli
 
 [Code](https://github.com/khamirdesai/RetViM) · [Checkpoint details](https://github.com/khamirdesai/RetViM/blob/main/docs/checkpoint.md) · [Citation](https://github.com/khamirdesai/RetViM/blob/main/CITATION.cff)
 
@@ -36,6 +36,14 @@ Khamir Desai · Sree Saicharan Vadapalli · Mayuri A. Mehta
 
 The supplied checkpoint uses **native 1D Mamba**, with pooling fusion and classifier dimensions that differ from the manuscript's summarized SS2D configuration. The linked implementation preserves the trained tensors and loads them strictly. It is a custom PyTorch model, not a Transformers `AutoModel` checkpoint.
 
+## Dataset and reported result
+
+[Kermany retinal OCT dataset](https://www.kaggle.com/datasets/paultimothymooney/kermany2018), with CNV, DME, DRUSEN and NORMAL labels. [Original dataset record](https://data.mendeley.com/datasets/rscbjbr9sj/2).
+
+The manuscript reports **99.90% accuracy (967/968 test images)**, the highest among the methods compared there. This is paper-reported, not an independently verified overall leaderboard ranking or a newly reproduced benchmark.
+
+Standalone [training and evaluation instructions](https://github.com/khamirdesai/RetViM/blob/main/docs/training.md) are available with the source. New training runs use explicit, hashed patient-disjoint manifests and are not claimed to recreate the original split.
+
 ## Inference
 
 ```bash