Update README.md

README.md

@@ -47,4 +47,33 @@ The seven numpy arrays store the spatial, temporal, and pose data for the rally:
 *   **`r_img.npy`**: A [T x 3] array containing the 2D ball tracking data per frame. The three columns represent the **`u`** (horizontal) coordinate, the **`v`** (vertical) coordinate, and a **visibility class**. The visibility class is binary, where 0 means the ball is out of frame/occluded, 1 means visible or hard to spot. The visibility class was directly extracke out of the TrackNet Dataset.
 *   **`2dPoseEstimation.npy`**: A [17 x 3] array containing the 2D human pose estimation of the hitting player. The rows correspond to the 17 [COCO-WholeBody keypoints](https://arxiv.org/abs/2007.11858), and the columns represent the **`u`** coordinate, **`v`** coordinate, and a model confidence **`score`**. For whole rallies, this pose is captured at the specific frame where the ball leaves the server's hand (or the first frame if the toss isn't visible).
 *   **`spin_class_per_shot.npy` & `spin_class_per_frame.npy`**: These files map the initial ball spin of the shots. The classes are categorized as **1 (topspin)**, **2 (backspin)**, and **0 (no spin)**, with 0 typically used for the initial ball toss. `spin_class_per_shot` provides an array of length [S] mapping one spin class to each of the *S* shots in the rally. spin_class_per_frame.npy has the length [T], which assigns each frame of the video a spin class according to the initial spin value for the respective shot.
-*   **`new_trajectory_frame_idx.npy`**: An array of length [S] providing the exact frame indices (time steps) at which each new trajectory begins and the corresponding new spin value can be measured.
+*   **`new_trajectory_frame_idx.npy`**: An array of length [S] providing the exact frame indices (time steps) at which each new trajectory begins and the corresponding new spin value can be measured.
+
+## Download and Usage
+
+This dataset utilizes Git LFS (Large File Storage) for binary `.npy` files. To ensure that the actual data is downloaded instead of small text pointers, please use the `huggingface_hub` library.
+
+### Installation
+``` bash
+pip install huggingface_hub numpy
+```
+
+### Dowload the full dataset
+```
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="XSpaceCoderX/ACE-Rallies", 
+    repo_type="dataset", 
+    local_dir="./data"
+)
+```
+
+### Loading the data
+
+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}")
+```