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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 13 new columns ({'test mAR', 'test rmse_pcutoff', 'train rmse', 'Shuffle number', 'Training epochs', 'test rmse', 'train mAP', 'Detector epochs (TD only)', 'train mAR', 'pcutoff', 'train rmse_pcutoff', 'test mAP', '%Training dataset'}) and 21 missing columns ({'Pablo.3', 'Pablo.16', 'Pablo.5', 'Pablo.15', 'Pablo.1', 'Pablo.4', 'Pablo.17', 'scorer', 'Unnamed: 1', 'Pablo.14', 'Pablo.11', 'Pablo.13', 'Pablo', 'Pablo.10', 'Unnamed: 2', 'Pablo.7', 'Pablo.6', 'Pablo.9', 'Pablo.8', 'Pablo.2', 'Pablo.12'}).

This happened while the csv dataset builder was generating data using

hf://datasets/jpcurrea/super-fly/evaluation-results-pytorch/iteration-0/CombinedEvaluation-results.csv (at revision 4fe6e57650c177150d958536cfd0e153c247c8b2), ['hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-0/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-0/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-20-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-0/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-30-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-1/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-1/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-30-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-10/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-10/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-180-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-11/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-11/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-180-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-12/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-12/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-150-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-2/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-2/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-60-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-3/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-3/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-30-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-4/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-4/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-40-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-5/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-5/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-50-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-6/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-6/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-30-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-7/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-7/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-100-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-8/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-8/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-110-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-9/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-9/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-110-results.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 784, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 795, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              %Training dataset: double
              Shuffle number: int64
              Training epochs: int64
              Detector epochs (TD only): int64
              pcutoff: double
              train rmse: double
              train rmse_pcutoff: double
              train mAP: double
              train mAR: double
              test rmse: double
              test rmse_pcutoff: double
              test mAP: double
              test mAR: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1910
              to
              {'scorer': Value('string'), 'Unnamed: 1': Value('string'), 'Unnamed: 2': Value('string'), 'Pablo': Value('string'), 'Pablo.1': Value('string'), 'Pablo.2': Value('string'), 'Pablo.3': Value('string'), 'Pablo.4': Value('string'), 'Pablo.5': Value('string'), 'Pablo.6': Value('string'), 'Pablo.7': Value('string'), 'Pablo.8': Value('string'), 'Pablo.9': Value('string'), 'Pablo.10': Value('string'), 'Pablo.11': Value('string'), 'Pablo.12': Value('string'), 'Pablo.13': Value('string'), 'Pablo.14': Value('string'), 'Pablo.15': Value('string'), 'Pablo.16': Value('string'), 'Pablo.17': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              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 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 13 new columns ({'test mAR', 'test rmse_pcutoff', 'train rmse', 'Shuffle number', 'Training epochs', 'test rmse', 'train mAP', 'Detector epochs (TD only)', 'train mAR', 'pcutoff', 'train rmse_pcutoff', 'test mAP', '%Training dataset'}) and 21 missing columns ({'Pablo.3', 'Pablo.16', 'Pablo.5', 'Pablo.15', 'Pablo.1', 'Pablo.4', 'Pablo.17', 'scorer', 'Unnamed: 1', 'Pablo.14', 'Pablo.11', 'Pablo.13', 'Pablo', 'Pablo.10', 'Unnamed: 2', 'Pablo.7', 'Pablo.6', 'Pablo.9', 'Pablo.8', 'Pablo.2', 'Pablo.12'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/jpcurrea/super-fly/evaluation-results-pytorch/iteration-0/CombinedEvaluation-results.csv (at revision 4fe6e57650c177150d958536cfd0e153c247c8b2), ['hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-0/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-0/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-20-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-0/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-30-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-1/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-1/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-30-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-10/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-10/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-180-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-11/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-11/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-180-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-12/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-12/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-150-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-2/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-2/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-60-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-3/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-3/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-30-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-4/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-4/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-40-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-5/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-5/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-50-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-6/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-6/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-30-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-7/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-7/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-100-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-8/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-8/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-110-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-9/CombinedEvaluation-results.csv', 'hf://datasets/jpcurrea/super-fly@4fe6e57650c177150d958536cfd0e153c247c8b2/evaluation-results-pytorch/iteration-9/SuperFlyJul29-trainset95shuffle1/DLC_Resnet50_SuperFlyJul29shuffle1_snapshot_best-110-results.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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.

scorer
string
Unnamed: 1
string
Unnamed: 2
string
Pablo
string
Pablo.1
string
Pablo.2
string
Pablo.3
string
Pablo.4
string
Pablo.5
string
Pablo.6
string
Pablo.7
string
Pablo.8
string
Pablo.9
string
Pablo.10
string
Pablo.11
string
Pablo.12
string
Pablo.13
string
Pablo.14
string
Pablo.15
string
Pablo.16
string
Pablo.17
string
bodyparts
null
null
neck
neck
tail_joint
tail_joint
tail_end
tail_end
antenna_left
antenna_left
antenna_right
antenna_right
head_left
head_left
head_right
head_right
shoulder_left
shoulder_left
shoulder_right
shoulder_right
coords
null
null
x
y
x
y
x
y
x
y
x
y
x
y
x
y
x
y
x
y
labeled-data
20250305_1120_fly_1_Drum_gain_30_led_0_rep_1_seq_1_vid
img0099.png
207.5777981282855
235.93020328369815
221.03317554115443
98.01258480179125
236.89130568654912
-4.626293842043507
226.22793540634314
311.12794834405736
172.31867454530152
306.9810821239772
255.25599894690396
238.8539942226609
156.91602858500391
235.89194692260367
262.95732192705276
225.22857664239763
153.3615718249352
213.3803874421687
labeled-data
20250305_1120_fly_1_Drum_gain_30_led_0_rep_1_seq_1_vid
img0143.png
206.6784232259654
235.89194692260367
222.081069186263
99.04536165995958
235.70648676652627
-11.735207362180862
228.00516378637747
312.3127672640802
175.87313130537018
308.75831050401155
256.44081786692686
241.81604152271814
155.13880020496958
236.48435638261512
262.36491246704134
225.22857664239763
152.76916236492377
215.15761582220304
labeled-data
20250305_1120_fly_1_Drum_gain_30_led_0_rep_1_seq_1_vid
img0197.png
211.01405246288107
233.22025424049002
230.69682051385723
119.1611368168848
253.407706726522
-1.4589032903791441
228.67807507273147
309.4278946429873
175.1813208828989
302.8669719593287
260.47331577046214
239.78117692414875
158.5266709936114
229.18276335823853
269.0529838952466
226.65933155683132
157.51729827304854
208.99530894698097
labeled-data
20250305_1120_fly_1_Drum_gain_30_led_0_rep_1_seq_1_vid
img0200.png
195.42264348574793
232.33749016253498
268.88141652716723
116.81764546030297
339.3781422685293
3.0750291381052897
184.7592732055419
306.9810821239772
133.21965018454603
275.5833807433706
238.6685340665835
257.21868748301574
145.06783938477497
206.27147392203136
250.5167232668124
244.77808882277537
154.5463907449581
187.90678066167652
labeled-data
20250305_1120_fly_1_Drum_gain_30_led_0_rep_1_seq_1_vid
img0258.png
222.081069186263
229.9678523224892
158.10084750502682
113.26318870023428
93.52821636377917
-1.6642465419862749
285.46888140748774
274.3985618233477
240.44576244661783
302.8342159038971
261.18009354701843
202.12460770195122
183.57445428551898
262.55037262311873
264.7345503070871
190.2764185017223
166.98698940519847
245.3704982827868
labeled-data
20250305_1120_fly_1_Drum_gain_30_led_0_rep_1_seq_1_vid
img0279.png
218.52661242619433
229.9678523224892
194.23782456572502
98.45295219994814
177.6503596854045
-16.474483042272432
259.4028651669841
291.5784361636796
214.3797462061142
306.3886726639658
263.54973138706424
216.93484420223737
169.9490367052557
246.5553172028097
268.2890070671558
205.08665500200846
161.65530426509548
224.63616718238617
labeled-data
20250305_1120_fly_1_Drum_gain_30_led_0_rep_1_seq_1_vid
img0321.png
231.20150687413866
217.07029071148398
138.84390294263528
167.1063410436215
4.5973311077724475
77.7768552738068
306.90446091635454
232.7155678802086
277.63265202003106
276.11859486441233
255.42645216764777
174.17195008756167
205.9671888600667
264.0061222176578
245.8374113223004
162.05947744080711
186.78910716937202
259.46394497512483
labeled-data
20250305_1120_fly_1_Drum_gain_30_led_0_rep_1_seq_1_vid
img0330.png
232.74443946646903
205.08665500200846
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End of preview.

SuperFly -- Magnotether Dataset for DeepLabCut

This repository hosts the community training dataset for tracking fruit fly body orientation in a magnetic tether using DeepLabCut (DLC). This serves as a living, evolving dataset where researchers can download training data or contribute new videos and annotation frames to iteratively improve the robustness of the underlying DLC model.

πŸ’» Code and Software Repository

The Python software, utility tools, and helper scripts developed to work with this model are hosted over on GitHub.

πŸ‘‰ Access the software repository here: https://github.com/jpcurrea/super_fly.git

Please open code-related bugs, feature requests, or software pull requests on the GitHub issues page.

πŸ“‚ Dataset Structure

This Hugging Face repository holds the heavy media assets and annotation files:

  • /videos/ β€” Raw behavioral training videos (.mp4 format).
  • /annotations/ β€” DeepLabCut-compatible labels, configurations (config.yaml), and labeled coordinates (.csv/.h5 format).

πŸ“œ Licensing

To maximize scientific collaboration while protecting the open nature of this research, this data operates under CC-BY-SA 4.0 (Creative Commons Attribution-ShareAlike 4.0).

  • What this means for the community: You are explicitly encouraged to download, use, and modify this tracking dataset. However, if you add new videos or annotations to this dataset to train your own models, you must share your additions publicly under this exact same license.
  • Note: The accompanying software tools on GitHub are licensed separately under the Apache License 2.0.

🀝 How to Contribute Data

We warmly welcome community data contributions to make this tracking model more robust across different setups.

To add your data to the master pipeline:

  1. Fork this Hugging Face dataset repository.
  2. Upload your raw videos to /videos/ and your corresponding DLC exported annotations (CSV/H5) into /annotations/.
  3. Submit a Pull Request back to this main repository. Once reviewed, your data will be merged into the unified training set.

✍️ How to Cite

If you use this dataset, the accompanying software, or the resulting model weights in your research, please use the following citation format:

@dataset{superfly,
  author    = {Currea, John Paul},
  title     = {DeepLabCut Behavior Tracking: A Living Community Dataset},
  year      = {2026},
  publisher = {Hugging Face Hub},
  version   = {1.0.0},
  url       = {https://huggingface.co}
}
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