Update README.md

README.md

@@ -19,9 +19,10 @@ size_categories:
 This dataset was created for the Master's thesis *"From Broadcast to 3D: A Deep Learning Approach for Tennis Trajectory and Spin Estimation"* by Alexandra Göppert at the University Augsburg, Chair of Machine Learning and Computer Vision. 
 
 This datasets serves as an enriched version of the original TrackNet Tennis dataset, supplementing the original broadcast tracking data with manual ball spin annotations and 2D human pose estimations for the hitting player. It was specifically designed to be used as a validation and test set (split 33% to 66% respectively) for the 2D-to-3D trajectory uplifting models available in the [tennisuplifting GitHub repository](https://github.com/xSpaceCoder).
-This dataset does not contain any images but uses primarily coordinates for indicating the position of the ball, the 16 court keypoints (see image) as well as the 17 COCO Whole Body keypoints of the hitting player on the respective image. 
+This dataset does not contain any images but uses primarily coordinates for indicating the position of the ball, the 16 court keypoints (see image) as well as the 17 COCO Whole Body keypoints (see end of file) of the hitting player on the respective image. 
+
+<img src="./keypoints.png" alt="Court Keypoints" height="200">
 
-![Alt text]()
 
 ### Original TrackNet Data
 The foundation of this dataset is the TrackNet dataset, which consists of broadcast tennis videos and 2D ball tracking data. The original dataset was introduced in [this paper](https://arxiv.org/abs/1907.03698) and downloaded from [Kaggle](https://www.kaggle.com/datasets/sofuskonglevoll/tracknet-tennis).
@@ -81,4 +82,6 @@ import numpy as np
 # Example: Loading a specific file after download
 data = np.load("./data/path/to/file.npy", allow_pickle=True)
 print(f"Data shape: {data.shape}")
-```
+```
+
+<img src="./COCO_whole_body_keypoints.png" alt="COCO WholeBody keypoints" style="width:50%; height:auto;" >