| --- |
| pretty_name: FineDiving Precise Event Spotting |
| license: other |
| license_name: finediving-research-terms |
| license_link: https://github.com/xujinglin/FineDiving/blob/main/agreement/Release_Agreement.pdf |
| task_categories: |
| - video-classification |
| tags: |
| - sports |
| - diving |
| - action-spotting |
| - precise-event-spotting |
| - opensportslib |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: train/metadata.parquet |
| - split: validation |
| path: valid/metadata.parquet |
| - split: test |
| path: test/metadata.parquet |
| --- |
| |
| # FineDiving precise event spotting |
|
|
| Complete 3,000-clip, four-event-class spotting release prepared for OpenSportsLib |
| and T-DEED reproduction. The original FineDiving AQA taxonomy has 52 action |
| classes; this spotting protocol uses Entry, Som(s).Pike, Som(s).Tuck and Twist(s). |
|
|
| | Split | Clips | |
| |---|---:| |
| | Train | 1,801 | |
| | Validation | 450 | |
| | Test | 749 | |
|
|
| ## Media and timing |
|
|
| Videos are FFV1 RGB-lossless Matroska files, packaged as OSL WebDataset TAR |
| shards with Parquet metadata. Every output frame was fully decoded and its RGB |
| pixels verified against the decoded source JPEG sequence. There are 312,256 |
| frames in total. Original JPEGs remain available in `sources/frames.zip`. |
|
|
| **25 fps is an explicit conversion clock, not a measured camera frame rate.** |
| Upstream labels use `fps: -1`. Original zero-based event `frame` indices are |
| retained; `position_ms = frame * 40`. Each record retains the upstream record, |
| source JPEG paths, lossless-video checksum, and decoded-RGB checksum. |
| Use frame-based tolerances for the precise event spotting benchmark. |
|
|
| This replaces an older metadata-only conversion whose video inputs were absent, |
| FPS was zero, and event timestamps were all zero. That conversion remains in |
| repository history; it must not be used for reproduction. The `raw` branch |
| retains the earlier native source annotations. |
|
|
| ## Loading |
|
|
| ```python |
| from opensportslib.tools import download_dataset_split_from_hf |
| download_dataset_split_from_hf( |
| repo_id="OpenSportsLab/OSL-loc-FineDiving", revision="main", |
| split="test", output_dir="data/finediving", download_format="parquet", |
| ) |
| ``` |
|
|
| Authentication and accepted repository access conditions are required. The |
| downloaded split contains a reconstructed OSL JSON manifest and unpacked videos. |
| Dataset Viewer rows contain OSL `sample_payload` JSON strings; they are not |
| direct Hugging Face `Video` columns. Original frame-indexed annotations are |
| under `annotations/`. See `reports/build.json` for validation and source hashes. |
|
|
| ## Sources and terms |
|
|
| - Original dataset: https://github.com/xujinglin/FineDiving |
| - Spotting labels: https://github.com/arturxe2/T-DEED/tree/d953b4de8a2ad941c565e1683c5b07255cbe644e/data/finediving |
| - Source frame mirror: https://huggingface.co/datasets/ZWJ666/FineDiving/tree/56adbfbc482ce5eda2aaa1033d7a3212613b6cfc |
| - The mirror traces the frame archive to OpenDataLab/FineDiving. The archive's |
| SHA-256 is recorded in the build report. |
|
|
| FineDiving's research and attribution terms apply; the software repository's MIT |
| license does not replace the dataset release agreement. OpenSportsLab confirmed |
| permission to mirror the dataset and annotations for this publication. The |
| existing access gate is preserved. Cite Xu et al., FineDiving (CVPR 2022), Hong |
| et al., Spotting Temporally Precise, Fine-Grained Events in Video (ECCV 2022), |
| and Xarles et al., T-DEED (CVPR Workshops 2024). |
|
|
