Hugging Face's logo

Datasets:
ml-resources
/
NICDF-AquaTrack

License:
Dataset card Files
xet
 Community
Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    TypeError
Message:      Value.__init__() missing 1 required positional argument: 'dtype'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
                  config_names = get_dataset_config_names(
                      path=dataset,
                      token=hf_token,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                      path,
                  ...<4 lines>...
                      **download_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1182, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1157, in dataset_module_factory
                  ).get_module()
                    ~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 692, in get_module
                  config_name: DatasetInfo.from_dict(dataset_info_dict)
                               ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 284, in from_dict
                  return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
                File "<string>", line 20, in __init__
                File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 170, in __post_init__
                  self.features = Features.from_dict(self.features)
                                  ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2023, in from_dict
                  obj = generate_from_dict(dic)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1602, in generate_from_dict
                  return {key: generate_from_dict(value) for key, value in obj.items()}
                               ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1621, in generate_from_dict
                  return class_type(**{k: v for k, v in obj.items() if k in field_names})
              TypeError: Value.__init__() missing 1 required positional argument: 'dtype'

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Mirror of NICDFcau/NICDF-AquaTrack from ModelScope. All credit to the original authors. Not an official release from them. License unchanged (cc-by-nc-4.0); non-commercial use only.

NICDF Aquatic Multi-Object Tracking Dataset (AquaTrack)

Release: NICDF-v1.0.0
Institutional owner: National Innovation Center for Digital Fishery (NICDF)
Public scope: train only

Overview

A train-only aquatic multi-object tracking dataset released by NICDF, with sequence-safe frame metadata, COCO annotations, MOTChallenge views where identity annotations are available, and stable NICDF_taxon_id labels.

This repository is the reviewed ModelScope delivery view for AquaTrack. It contains training data only; validation, public-test, private-test, and evaluator-only material are not included.

Dataset scale

  • Training media records: 62,401
  • Unique asset_id values: 62,401
  • Unique media paths: 62,401
  • Formal species identity: NICDF_taxon_id for one species or NICDF_taxon_ids for mixed-species records

The COCO view covers 62,401 images and 535,127 annotations. 35 complete sequences provide MOTChallenge identity annotations. Filter tracking_annotation_available == true for genuine MOT data; rows with annotation_task_scope == detection_only contain detection boxes but no track IDs. For RLE segmentations, area is the foreground-pixel count; otherwise it is bounding-box area.

Repository layout 

.
|-- README.md
|-- LICENSE
|-- RELEASE.json
|-- data/train.csv
|-- media/sha256/...
|-- annotations/AquaTrack/...
|-- metadata/...
|-- checksums.sha256
`-- checksums.modelscope.sha256

Each row in data/train.csv represents one training media asset. Core fields are:

  • asset_id
  • image
  • sha256
  • file_size_bytes
  • media_type
  • modality
  • width
  • height
  • source_dataset
  • source_partition
  • session_id
  • sequence_id
  • frame_index
  • global_split
  • split_basis
  • annotation_set
  • NICDF_taxon_id
  • NICDF_taxon_ids
  • taxon_count
  • taxonomy_status
  • canonical_name_zh
  • scientific_name
  • taxonomic_rank
  • label
  • label_name
  • rights_status
  • rights_id
  • data_license
  • institutional_owner
  • publication_visibility
  • tracking_annotation_available
  • annotation_task_scope
  • detection_annotation_available
  • annotation_complete
  • training_eligible
  • mot_sequence
  • mot_annotation_count
  • mot_registry
  • track_states_table
  • coco_annotations

image is a repository-relative path. Missing contextual fields are left empty and are not inferred. Site, batch, pond/tank, and date are not ModelScope-required fields.

Loading 

from modelscope.msdatasets import MsDataset

dataset = MsDataset.load(
    "NICDFcau/NICDF-AquaTrack",
    subset_name="default",
    split="train",
)

The COCO, semantic-mask, and MOTChallenge files under annotations/ are the authoritative task views. The platform table preview does not replace task-format parsing.

Split and leakage policy

This repository exposes only default/train. NICDF validation and test partitions remain in the private evaluation chain and are not uploaded. Complete site, batch, session, event, and sequence groups are kept together.

Taxonomy

Published records use reviewed NICDF_taxon_id values and include canonical Chinese names, scientific names, and taxonomic ranks. Koi cultivars and common carp share the species-level identity Cyprinus carpio; cultivar information remains in label views and is not presented as a separate species.

Ownership and licence

  • Institutional owner: National Innovation Center for Digital Fishery (NICDF)
  • Data licence: CC BY-NC 4.0
  • Redistribution scope: train-only release

Use of this dataset must comply with LICENSE.

Citation

Until a related paper or DOI is issued, cite the institutional dataset record:

National Innovation Center for Digital Fishery. (2026). NICDF Aquatic Multi-Object Tracking Dataset (AquaTrack) (NICDF-v1.0.0) [Dataset]. ModelScope.

Known limitations

  • Only training data are provided; test labels are not public.
  • Sessions with verified source annotation gaps and one complete sequence affected by a ModelScope delivery block were excluded and are listed in metadata/release_exclusions.csv.
  • Image quality, viewpoint, density, and occlusion vary across sources.
  • The same canonical media asset may be reused across NICDF task datasets through a stable asset_id and SHA-256.

Integrity

checksums.sha256 records the canonical NICDF source bytes before upload. ModelScope may normalize line endings in text files such as XML and Markdown. For a repository downloaded from ModelScope, verify against checksums.modelscope.sha256, which records the platform-delivered bytes and covers all release files except itself and the platform-maintained dataset_infos.json. Both manifests are provided so that source provenance and downloadable-byte reproducibility remain independently auditable.
Downloads last month
37
Total file size:
12.5 GB

Collections including ml-resources/NICDF-AquaTrack