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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 19 new columns ({'train/cls_loss', 'lr/pg6', 'val/box_loss', 'lr/pg2', 'time', 'lr/pg3', 'lr/pg4', 'val/cls_loss', 'lr/pg0', 'metrics/precision(B)', 'train/box_loss', 'lr/pg7', 'metrics/mAP50(B)', 'lr/pg1', 'metrics/mAP50-95(B)', 'val/dfl_loss', 'metrics/recall(B)', 'lr/pg5', 'train/dfl_loss'}) and 7 missing columns ({'det_accuracy', 'mAP50-95', 'f1', 'recall', 'mAP50', 'fitness', 'precision'}).

This happened while the csv dataset builder was generating data using

hf://datasets/TamAko783/n-rdd2024-augmented/runs/yolo26_distilled_frdc/results.csv (at revision 9203f30bc281d52a63e67ad266c005af6ceadcac), ['hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26_distilled_frdc/metrics_distill.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26_distilled_frdc/results.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26n_fresh_gcp/metrics_full.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26n_fresh_gcp/results.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26n_nomosaic_ft/metrics_finetune.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26n_nomosaic_ft/results.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26n_p2_v2/metrics_v2.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26n_p2_v2/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/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              epoch: int64
              time: double
              train/box_loss: double
              train/cls_loss: double
              train/dfl_loss: double
              metrics/precision(B): double
              metrics/recall(B): double
              metrics/mAP50(B): double
              metrics/mAP50-95(B): double
              val/box_loss: double
              val/cls_loss: double
              val/dfl_loss: double
              lr/pg0: double
              lr/pg1: double
              lr/pg2: double
              lr/pg3: double
              lr/pg4: double
              lr/pg5: double
              lr/pg6: double
              lr/pg7: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2691
              to
              {'epoch': Value('int64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'det_accuracy': Value('float64'), 'mAP50': Value('float64'), 'mAP50-95': Value('float64'), 'fitness': Value('float64')}
              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 1343, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1802, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              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 19 new columns ({'train/cls_loss', 'lr/pg6', 'val/box_loss', 'lr/pg2', 'time', 'lr/pg3', 'lr/pg4', 'val/cls_loss', 'lr/pg0', 'metrics/precision(B)', 'train/box_loss', 'lr/pg7', 'metrics/mAP50(B)', 'lr/pg1', 'metrics/mAP50-95(B)', 'val/dfl_loss', 'metrics/recall(B)', 'lr/pg5', 'train/dfl_loss'}) and 7 missing columns ({'det_accuracy', 'mAP50-95', 'f1', 'recall', 'mAP50', 'fitness', 'precision'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/TamAko783/n-rdd2024-augmented/runs/yolo26_distilled_frdc/results.csv (at revision 9203f30bc281d52a63e67ad266c005af6ceadcac), ['hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26_distilled_frdc/metrics_distill.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26_distilled_frdc/results.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26n_fresh_gcp/metrics_full.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26n_fresh_gcp/results.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26n_nomosaic_ft/metrics_finetune.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26n_nomosaic_ft/results.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26n_p2_v2/metrics_v2.csv', 'hf://datasets/TamAko783/n-rdd2024-augmented@9203f30bc281d52a63e67ad266c005af6ceadcac/runs/yolo26n_p2_v2/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.

epoch
int64
precision
float64
recall
float64
f1
float64
det_accuracy
float64
mAP50
float64
mAP50-95
float64
fitness
float64
1
0.4638
0.4169
0.4391
0.2814
0.4327
0.2238
0.2447
2
0.4524
0.4083
0.4292
0.2733
0.42
0.2154
0.2359
3
0.4182
0.3714
0.3934
0.2449
0.3669
0.1751
0.1943
4
0.4168
0.3738
0.3941
0.2454
0.3683
0.1781
0.1971
5
0.428
0.3993
0.4131
0.2604
0.395
0.1948
0.2148
6
0.4525
0.397
0.4229
0.2682
0.4042
0.2012
0.2215
7
0.4538
0.4005
0.4255
0.2703
0.4029
0.2016
0.2217
8
0.4458
0.4103
0.4273
0.2717
0.4158
0.2078
0.2286
9
0.4607
0.4138
0.436
0.2788
0.4227
0.2137
0.2346
10
0.458
0.411
0.4333
0.2765
0.4255
0.218
0.2387
11
0.4537
0.4272
0.4401
0.2821
0.4293
0.2207
0.2415
12
0.4592
0.4267
0.4423
0.284
0.4301
0.221
0.2419
13
0.4711
0.4167
0.4422
0.2839
0.433
0.2223
0.2434
14
0.464
0.4211
0.4415
0.2833
0.4361
0.2256
0.2466
15
0.4693
0.4192
0.4429
0.2844
0.4371
0.2274
0.2484
16
0.4614
0.4263
0.4431
0.2846
0.4374
0.2289
0.2497
17
0.4645
0.4298
0.4465
0.2874
0.4376
0.2293
0.2501
18
0.4686
0.4259
0.4462
0.2872
0.4384
0.2306
0.2513
19
0.4686
0.4273
0.447
0.2878
0.4396
0.2313
0.2521
20
0.4733
0.4267
0.4488
0.2893
0.4408
0.2316
0.2525
21
0.4756
0.4267
0.4498
0.2902
0.4408
0.2314
0.2523
22
0.4773
0.4218
0.4478
0.2885
0.4409
0.2319
0.2528
23
0.4776
0.4244
0.4494
0.2899
0.4414
0.2322
0.2531
24
0.4761
0.4267
0.45
0.2903
0.4423
0.2324
0.2534
25
0.4727
0.4293
0.4499
0.2903
0.442
0.2323
0.2533
26
0.4752
0.427
0.4498
0.2902
0.442
0.2321
0.2531
27
0.4755
0.4286
0.4509
0.291
0.4423
0.2326
0.2535
28
0.4721
0.431
0.4506
0.2908
0.4431
0.2332
0.2542
29
0.4693
0.4343
0.4511
0.2912
0.4436
0.2338
0.2547
30
0.4733
0.4289
0.45
0.2903
0.4438
0.234
0.255
31
0.4728
0.4285
0.4496
0.29
0.4431
0.2339
0.2548
32
0.471
0.4314
0.4503
0.2906
0.4431
0.2339
0.2549
33
0.4697
0.4335
0.4509
0.291
0.4444
0.2347
0.2557
34
0.4704
0.4359
0.4525
0.2924
0.4451
0.2353
0.2562
35
0.4701
0.4358
0.4523
0.2922
0.4452
0.235
0.256
36
0.4655
0.4357
0.4501
0.2904
0.4446
0.2348
0.2558
37
0.4689
0.4316
0.4495
0.2899
0.4442
0.2348
0.2557
38
0.4686
0.4324
0.4498
0.2901
0.4433
0.2339
0.2549
39
0.4709
0.4331
0.4512
0.2913
0.4442
0.2348
0.2557
40
0.4773
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0.4441
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0.2563
41
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42
0.4766
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0.2568
43
0.4736
0.4289
0.4501
0.2904
0.4449
0.2356
0.2566
44
0.4844
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0.2916
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45
0.4845
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46
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47
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48
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49
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50
0.4786
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0.2382
0.2592
51
0.4802
0.4259
0.4514
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0.2593
52
0.4768
0.4292
0.4517
0.2918
0.4488
0.2389
0.2599
53
0.4781
0.4289
0.4522
0.2921
0.4493
0.2392
0.2602
54
0.477
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0.4524
0.2923
0.4496
0.2391
0.2602
55
0.4801
0.4296
0.4535
0.2932
0.4504
0.2394
0.2605
56
0.477
0.4289
0.4516
0.2917
0.45
0.2391
0.2602
57
0.4812
0.4276
0.4528
0.2927
0.4496
0.2388
0.2599
58
0.49
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0.2944
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0.2395
0.2606
59
0.4907
0.4246
0.4552
0.2947
0.4512
0.2402
0.2613
60
0.4911
0.4248
0.4555
0.2949
0.4513
0.2401
0.2612
61
0.4907
0.426
0.4561
0.2954
0.451
0.2401
0.2612
62
0.4948
0.4233
0.4562
0.2955
0.4511
0.2404
0.2614
63
0.4848
0.4311
0.4564
0.2957
0.4512
0.2407
0.2617
64
0.4912
0.4265
0.4566
0.2958
0.4506
0.2409
0.2619
65
0.4849
0.4306
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66
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0.2947
0.4514
0.2413
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67
0.489
0.428
0.4564
0.2957
0.4512
0.2414
0.2624
68
0.4842
0.4317
0.4564
0.2957
0.4505
0.2412
0.2621
69
0.4829
0.4326
0.4564
0.2956
0.451
0.2422
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70
0.4814
0.435
0.457
0.2962
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71
0.4813
0.4331
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72
0.4825
0.4323
0.456
0.2953
0.4506
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0.2627
73
0.4835
0.4336
0.4572
0.2963
0.4511
0.2421
0.263
74
0.4984
0.4224
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0.2627
75
0.4915
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0.2423
0.2633
76
0.4909
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0.4569
0.2961
0.4519
0.243
0.2639
77
0.4906
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0.4554
0.2949
0.4521
0.2431
0.264
78
0.4889
0.4267
0.4557
0.295
0.4514
0.2426
0.2635
79
0.4926
0.4244
0.4559
0.2953
0.4507
0.242
0.2629
80
0.4964
0.4231
0.4568
0.296
0.4513
0.2423
0.2632
81
0.493
0.4264
0.4573
0.2964
0.4509
0.2424
0.2632
82
0.4993
0.4197
0.4561
0.2954
0.4513
0.2427
0.2635
83
0.4987
0.422
0.4572
0.2963
0.4522
0.2431
0.264
84
0.4961
0.424
0.4572
0.2964
0.4527
0.2432
0.2641
85
0.5067
0.4172
0.4576
0.2967
0.4532
0.2434
0.2644
86
0.5102
0.4145
0.4574
0.2965
0.4531
0.2435
0.2644
87
0.5116
0.4151
0.4583
0.2973
0.4532
0.2433
0.2643
88
0.5046
0.422
0.4596
0.2984
0.4526
0.2429
0.2639
89
0.5033
0.422
0.4591
0.2979
0.4545
0.2437
0.2648
90
0.5035
0.4213
0.4587
0.2976
0.4545
0.2437
0.2648
91
0.5064
0.4205
0.4595
0.2982
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0.2434
0.2645
92
0.5065
0.4199
0.4591
0.298
0.4547
0.2444
0.2654
93
0.5029
0.4231
0.4596
0.2983
0.4548
0.2443
0.2654
94
0.4962
0.4247
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0.4549
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95
0.502
0.423
0.4591
0.298
0.4548
0.2444
0.2655
96
0.4994
0.4234
0.4582
0.2972
0.4548
0.2445
0.2655
97
0.5071
0.4176
0.458
0.297
0.4543
0.2447
0.2656
98
0.502
0.421
0.4579
0.297
0.4544
0.2447
0.2656
99
0.4968
0.4225
0.4567
0.2959
0.4541
0.2446
0.2656
100
0.4986
0.4225
0.4574
0.2965
0.454
0.2445
0.2654
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