README.md

metadata

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

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.