Upload README.md with huggingface_hub

README.md

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+---
+license: other
+task_categories:
+- other
+---
+
+# SoccerNet Features
+
+Pre-extracted per-game features for the [SoccerNet](https://www.soccer-net.org) benchmark, structured as `<league>/<season>/<game>/<file>`, one file per game half (`1_...`/`2_...`).
+
+This `main` branch holds no data — each feature type lives on its own branch so you only download what you need:
+
+| Branch | Files | Description |
+|---|---|---|
+| `baidu-soccer-embeddings` | `{1,2}_baidu_soccer_embeddings.npy` | Frame embeddings from [baidu-research/vidpress-sports](https://github.com/baidu-research/vidpress-sports), used by the Action Spotting and Dense Video Captioning 2023 challenges |
+| `resnet-tf2` | `{1,2}_ResNET_TF2.npy` | ResNET features @2fps, extracted with TF2 ([SoccerNetv2-DevKit](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)) |
+| `resnet-tf2-pca512` | `{1,2}_ResNET_TF2_PCA512.npy` | Same as above, dimensionality-reduced to 512 with PCA |
+| `player-boundingbox-maskrcnn` | `{1,2}_player_boundingbox_maskrcnn.json` | Player bounding boxes @2fps, extracted with MaskRCNN |
+| `field-calib-ccbv` | `{1,2}_field_calib_ccbv.json` | Field camera calibration @2fps, extracted with CCBV |
+
+## Download
+
+Using the [SoccerNet pip package](https://pypi.org/project/SoccerNet/) (recommended — matches the local folder layout used by the rest of the `SoccerNet.Downloader` API):
+
+```python
+from SoccerNet.Downloader import SoccerNetDownloader
+d = SoccerNetDownloader(LocalDirectory="path/to/soccernet")
+d.downloadDataTask(task="spotting-2023", split=["train", "valid", "test", "challenge"])
+d.downloadDataTask(task="caption-2023", split=["train", "valid", "test", "challenge"])
+```
+
+Directly with `huggingface_hub`, picking a branch and (optionally) a subset of games via `allow_patterns`:
+
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(
+    repo_id="SoccerNet/SN-Features",
+    repo_type="dataset",
+    revision="resnet-tf2-pca512",   # one of the branches listed above
+    local_dir="path/to/soccernet",
+)
+```
+
+Corresponding labels (`Labels-v2.json`, `Labels-caption.json`) are in [`SoccerNet/SN-Labels`](https://huggingface.co/datasets/SoccerNet/SN-Labels). Held-out test/challenge ground truth is in the private [`SoccerNet/SN-GroundTruth`](https://huggingface.co/datasets/SoccerNet/SN-GroundTruth).