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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+---
+language: en
+tags:
+  - self-supervised-learning
+  - echocardiography
+  - medical-imaging
+  - video-representation
+license: apache-2.0
+datasets:
+  - EchoDynamic
+  - RVENet
+  - EchoNet-Pediatric-LVH
+library_name: pytorch
+model_index: deep-learning
+paper: https://arxiv.org/pdf/2506.11777
+---
+
+# 🫀 DISCOVR (checkpoint: `DISCOVR_399_Adult_Pediatric`)
+
+**Paper:** *Self-Supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation* — NeurIPS 2025  
+📄 [arXiv:2506.11777](https://arxiv.org/pdf/2506.11777)
+
+**Hugging Face repo:** `Div97/DISCOVR_399_Adult_Pediatric`  
+**This checkpoint:** DISCOVR trained for **400 epochs** on adult and pediatric echocardiography datasets (EchoDynamic, RVENet, EchoNet-Pediatric LVH).  
+
+> **Short description**  
+> This checkpoint contains the DISCOVR backbone trained with self-supervised online cluster distillation to learn spatio-temporal cardiac video representations. It captures fine-grained anatomical semantics and global temporal dynamics for downstream tasks such as anomaly detection, classification, and segmentation. **Not for clinical use.**
+
+---
+
+## 🔖 Quick facts
+- **Model name (repo):** `Div97/DISCOVR_399_Adult_Pediatric`  
+- **Training:** 400 epochs, ViT-Base backbone, 64-frame clips (stride 3)  
+- **Datasets used (training):** EchoDynamic, RVENet, EchoNet-Pediatric LVH  
+- **Intended use:** Research & educational purposes; downstream representation learning for echocardiography  
+- **Not intended for:** Clinical diagnosis, medical decision-making
+
+---