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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    IndexError
Message:      list index out of range
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1859, in _prepare_split_single
                  original_shard_lengths[original_shard_id] += len(table)
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
              IndexError: list index out of range
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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End of preview.

Microduck Detection Dataset

Single-class object detection dataset (class 0 = microduck, the 25 cm biped robot duck announced by Pollen Robotics / Hugging Face on 2026-08-27) in YOLO format, built to train a tiny (<5M param) detector.

Composition

  • Synthetic scene renders (train ~1,040 / val ~60): the robot from the open-source MuJoCo model in pollen-robotics/microduck_rl (full visual-mesh model, scene_allcollisions.xml), rendered headlessly with randomized pose (STAND/SIT/FOLD keyframes + joint noise + yaw), all four official colorways (Cream/Graphite/Lavender/Sky), camera distance/azimuth/elevation/FOV, lighting and floor color. Boxes are exact, obtained by projecting all 140 robot-body geom meshes into the camera.
  • Synthetic composites (train ~655 / val ~45): the robot cutout (segmented by a robotless render diff, feathered edges) pasted onto ~220 CC0 background photos (picsum.photos) with random scale, position, flip, brightness, 12% occlusion rectangles. Boxes are pixel-exact from the pasted alpha mask.
  • Synthetic negatives (train ~225 / val ~20): empty floor renders and background-only images with empty labels.
  • Real press-kit photos (8 train, 4 val): 13 official launch photos from the Pollen Robotics microduck press kit, pre-labeled by an ensemble of two zero-shot detectors (OWLv2 ∩ Grounding DINO, IoU≥0.35 agreement), threshold ≥0.4 (team photo keeps 2 boxes at ≥0.25 — it shows two robots). Train real photos are duplicated 15x to balance against the synthetic majority. microduck-skate.jpg had no confident box and is excluded from labels (kept for qualitative checks).

Splits

split images
train 2,048
val 134

Files

  • images/{train,val}/*.jpg, labels/{train,val}/*.txt (YOLO: class cx cy w h, normalized)
  • data.yaml, gen_meta.json (per-sample generation parameters)

Caveats

  • Synthetic robot appearance comes from the open-source MuJoCo mesh model; real-photo coverage is only the 13 press photos, so expect the remaining gap to be real-world variation.
  • Zero-shot labels on the 4 real val images are model-ensemble-verified but not human-verified; treat real-photo mAP as approximate.
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