Update README.md

README.md

@@ -24,22 +24,22 @@ paper: https://arxiv.org/pdf/2506.11777
 
 ## 📦 Available Checkpoints
 
-| Epochs | Checkpoint | Hugging Face Repo |
-|:-------:|:------------|:------------------|
-| 200 | `DISCOVR_199_Adult_Pediatric` | [`Div97/DISCOVR_199_Adult_Pediatric`](https://huggingface.co/Div97/DISCOVR_199_Adult_Pediatric) |
-| 300 | `DISCOVR_299_Adult_Pediatric` | [`Div97/DISCOVR_299_Adult_Pediatric`](https://huggingface.co/Div97/DISCOVR_299_Adult_Pediatric) |
-| 400 | `DISCOVR_399_Adult_Pediatric` | [`Div97/DISCOVR_399_Adult_Pediatric`](https://huggingface.co/Div97/DISCOVR_399_Adult_Pediatric) |
-| 600 | `DISCOVR_599_Adult_Pediatric` | [`Div97/DISCOVR_599_Adult_Pediatric`](https://huggingface.co/Div97/DISCOVR_599_Adult_Pediatric) |
-| 800 | `DISCOVR_799_Adult_Pediatric` | [`Div97/DISCOVR_799_Adult_Pediatric`](https://huggingface.co/Div97/DISCOVR_799_Adult_Pediatric) |
+| Epochs | Filename | Description |
+|:-------:|:-----------|:-------------|
+| 200 | `checkpoint-199.pth` | Model trained for ~200 epochs |
+| 300 | `checkpoint-299.pth` | Model trained for ~300 epochs |
+| 400 | `checkpoint-399.pth` | Model trained for ~400 epochs |
+| 600 | `checkpoint-599.pth` | Model trained for ~600 epochs |
+| 800 | `checkpoint-799.pth` | Model trained for ~800 epochs |
 
-> Each checkpoint corresponds to a model trained for **~200, 300, 400, 600, and 800 epochs**, respectively, on **adult and pediatric echocardiography datasets** (EchoDynamic, RVENet, EchoNet-Pediatric-LVH).
+> Each checkpoint corresponds to a model trained for the indicated number of epochs on **adult and pediatric echocardiography datasets** (EchoDynamic, RVENet, EchoNet-Pediatric LVH).
 
 ---
 
 ## 🧠 Model Overview
 
 DISCOVR is a self-supervised framework for learning spatio-temporal echocardiographic video representations via **online cluster distillation**.  
-The model learns to capture both fine-grained anatomical semantics and global temporal dynamics, supporting downstream tasks such as:
+It learns both fine-grained anatomical semantics and global temporal dynamics, supporting downstream tasks such as:
 - Cardiac view classification  
 - Functional abnormality detection  
 - Video segmentation  
@@ -50,7 +50,8 @@ The model learns to capture both fine-grained anatomical semantics and global te
 ---
 
 ## 🔖 Quick Facts
-- **Model family:** `DISCOVR_[199/299/399/599/799]_Adult_Pediatric`  
+- **Repo:** `Div97/DISCOVR_ADULT_PEDIATRIC_MODEL`  
+- **Model family:** DISCOVR checkpoints (199 → 799)  
 - **Architecture:** ViT-Base backbone, 64-frame clips (stride 3)  
 - **Datasets used:** EchoDynamic, RVENet, EchoNet-Pediatric LVH  
 - **Training objective:** Self-supervised online cluster distillation