| --- |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train.parquet |
| - split: test |
| path: data/test.parquet |
| task_categories: |
| - object-detection |
| --- |
| |
| # eAP Detection Benchmark Dataset |
|
|
| This public dataset repo contains eAP detection inputs for the released train/test benchmark splits. |
|
|
| ## Files |
|
|
| - `data/train.parquet`: public `train` split metadata. |
| - `data/test.parquet`: public `test` split metadata. |
| - `data/<split>/<sequence_id>/events.h5`: event stream for the sequence. |
| - `data/<split>/<sequence_id>/rgb_shards/rgb-00000.tar`: RGB PNG tar shards. |
| - `data/train/<sequence_id>/labels.parquet`: public train labels. |
| - `sample_submission.json`: eAP-native submission template. |
|
|
| Test labels are not included in this public dataset. Evaluation is handled by the CodaBench challenge. |
|
|
| ## Parquet Columns |
|
|
| `sample_token`, `split`, `sequence_id`, `rgb_shard_path`, `rgb_member_path`, `events_path`, `labels_path`, |
| `rgb_exposure_start_timestamp_us`, `rgb_exposure_end_timestamp_us`, `K_event`, `T_event_ego`. |
|
|
| `rgb_shard_path` is relative to the repository root. `rgb_member_path` is the member path inside that tar. |
| Tar members use `rgb/<image_name>.png`; events remain as one `events.h5` file per sequence and are not tarred. |
| For test rows, `labels_path` is null by design. |
|
|
| ## Loading Example |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("parquet", data_files={"train": "data/train.parquet", "test": "data/test.parquet"}) |
| ``` |
|
|
| ## Submission Format |
|
|
| Submit a JSON object with `meta` and `results`. `results` is keyed by `sample_token`; each detection uses: |
| `class_name`, `translation_m` (`[x, y, z]`), `size_lwh_m` (`[length, width, height]`), `yaw_rad`, |
| `velocity_mps` (`[vx, vy]`), and `score`. |
|
|
| ## Splits |
|
|
| - `train`: 118247 samples from 40 sequences. |
| - `test`: 35940 samples from 12 sequences. |
|
|
