Update README.md

README.md

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 ### Dataset Summary
 
-The **ProxiBall Testbench** is a highly curated, single-class benchmark dataset designed explicitly to evaluate object detection models on close-proximity, high-speed soccer balls. It serves as the official held-out evaluation split for the IEEE conference paper, *"Close-Proximity High-Speed Soccer Ball Detection: Benchmarking and Data Augmentation Study."*
+The **ProxiBall Testbench** is a Test subset of the main **ProxiBall** dataset (>18k images), used as a testbench in evaluation. Testbench set consists of unique annotated video frames other than in the main set, but strictly in the same environmental setup: indoor training arenas with artificial glass, artificial lightning, and close-proximity ball (2-10 meters from camera to ball). It is a highly curated, single-class benchmark dataset designed explicitly to evaluate object detection models on close-proximity, high-speed soccer balls. It serves as the official held-out evaluation split for the IEEE conference paper, *"Close-Proximity High-Speed Soccer Ball Detection: Benchmarking and Data Augmentation Study."*
 
 Standard open-source sports datasets (e.g., SoccerNet, ISSIA-CNR) predominantly feature long-shot or broadcast-style views. In contrast, indoor training arenas (like Footbonaut or Footbot) feature compact scene geometry. When a soccer ball is struck at high speeds close to the camera, it undergoes extreme geometric deformation, resulting in elongated motion blur. This testbench isolates this specific domain gap, challenging models to maintain high precision and recall on severe edge cases where standard models usually fail.
 
 - **Paper:** Close-Proximity High-Speed Soccer Ball Detection: Benchmarking and Data Augmentation Study (IEEE 2026)
 - **Authors:** Altay Kairat, Azamat Shmitov, Yessimkhan Orynbay, and Arlen Smagulov
-- **Point of Contact:** altay.kairat@nu.edu.kz
+- **Point of Contact:** kairataltay@gmail.com
 
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