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Datasets:
jarvisit
/
ham10000-skin-lesions

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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 785 new columns ({'pixel0662', 'pixel0661', 'pixel0269', 'pixel0029', 'pixel0321', 'pixel0526', 'pixel0328', 'pixel0389', 'pixel0237', 'pixel0354', 'pixel0000', 'pixel0253', 'pixel0746', 'pixel0198', 'pixel0565', 'pixel0756', 'pixel0059', 'pixel0407', 'pixel0483', 'pixel0101', 'pixel0363', 'pixel0053', 'pixel0118', 'pixel0293', 'pixel0044', 'pixel0370', 'pixel0338', 'pixel0762', 'pixel0154', 'pixel0343', 'pixel0577', 'pixel0352', 'pixel0061', 'pixel0069', 'pixel0151', 'pixel0573', 'pixel0581', 'pixel0434', 'pixel0259', 'pixel0575', 'pixel0129', 'pixel0379', 'pixel0707', 'pixel0256', 'pixel0394', 'pixel0547', 'pixel0537', 'pixel0592', 'pixel0623', 'pixel0591', 'pixel0294', 'pixel0240', 'pixel0320', 'pixel0035', 'pixel0578', 'pixel0764', 'pixel0016', 'pixel0401', 'pixel0382', 'pixel0486', 'pixel0258', 'pixel0210', 'pixel0677', 'pixel0457', 'pixel0779', 'pixel0247', 'pixel0712', 'pixel0479', 'pixel0026', 'pixel0620', 'pixel0682', 'pixel0624', 'pixel0392', 'pixel0466', 'pixel0741', 'pixel0177', 'pixel0721', 'pixel0639', 'pixel0748', 'pixel0215', 'pixel0440', 'pixel0406', 'pixel0566', 'pixel0590', 'pixel0124', 'pixel0236', 'pixel0263', 'pixel0042', 'pixel0579', 'pixel0228', 'pixel0651', 'pixel0727', 'pixel0369', 'pixel0763', 'pixel0179', 'pixel0255', 'pixel0311', 'pixel0141', 'pixel0170', 'pixel0307', 'pixel0371', 'pixel0464', 'pixel0108', 'pixel0221', 'pixel0298', 'pixel0368', 'pixel0729', 'pixel0743', 'pixel0648', 'pixel0199', 'pixel0642', 'pixel0010', 'pixel0071', 'pixel0014', 'pixel0265', 'pix
...
461', 'pixel0614', 'pixel0130', 'pixel0373', 'pixel0378', 'pixel0652', 'pixel0374', 'pixel0641', 'pixel0052', 'pixel0395', 'pixel0087', 'pixel0731', 'pixel0691', 'pixel0549', 'pixel0217', 'pixel0011', 'pixel0063', 'pixel0515', 'pixel0169', 'pixel0542', 'pixel0765', 'pixel0266', 'pixel0171', 'pixel0271', 'pixel0090', 'pixel0173', 'pixel0067', 'pixel0219', 'pixel0416', 'pixel0523', 'pixel0197', 'pixel0292', 'pixel0319', 'pixel0336', 'pixel0443', 'pixel0012', 'pixel0699', 'pixel0524', 'pixel0144', 'pixel0643', 'pixel0740', 'pixel0273', 'pixel0162', 'pixel0268', 'pixel0233', 'pixel0602', 'pixel0514', 'pixel0384', 'pixel0504', 'pixel0127', 'pixel0732', 'pixel0153', 'pixel0733', 'pixel0558', 'pixel0655', 'pixel0413', 'pixel0409', 'pixel0242', 'pixel0032', 'pixel0244', 'pixel0249', 'pixel0286', 'pixel0350', 'pixel0444', 'pixel0454', 'pixel0462', 'pixel0453', 'pixel0039', 'pixel0351', 'pixel0033', 'pixel0398', 'pixel0637', 'pixel0077', 'pixel0023', 'pixel0125', 'pixel0397', 'pixel0760', 'pixel0728', 'pixel0709', 'pixel0201', 'pixel0569', 'pixel0674', 'pixel0657', 'pixel0099', 'pixel0645', 'pixel0004', 'pixel0506', 'pixel0100', 'pixel0225', 'pixel0783', 'pixel0318', 'pixel0231', 'pixel0660', 'pixel0516', 'pixel0323', 'pixel0076', 'pixel0759', 'pixel0175', 'pixel0190', 'pixel0212', 'pixel0234', 'pixel0040', 'pixel0487', 'pixel0043', 'pixel0507', 'pixel0739', 'pixel0715', 'pixel0425', 'pixel0267', 'pixel0463', 'pixel0527', 'pixel0278', 'pixel0616', 'pixel0145', 'pixel0204', 'pixel0181'}) and 7 missing columns ({'localization', 'dx', 'sex', 'lesion_id', 'age', 'image_id', 'dx_type'}).

This happened while the csv dataset builder was generating data using

hf://datasets/jarvisit/ham10000-skin-lesions/hmnist_28_28_L.csv (at revision 5004e634101c4773c2d59c83939fa57eb0ab1f76)

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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              pixel0000: int64
              pixel0001: int64
              pixel0002: int64
              pixel0003: int64
              pixel0004: int64
              pixel0005: int64
              pixel0006: int64
              pixel0007: int64
              pixel0008: int64
              pixel0009: int64
              pixel0010: int64
              pixel0011: int64
              pixel0012: int64
              pixel0013: int64
              pixel0014: int64
              pixel0015: int64
              pixel0016: int64
              pixel0017: int64
              pixel0018: int64
              pixel0019: int64
              pixel0020: int64
              pixel0021: int64
              pixel0022: int64
              pixel0023: int64
              pixel0024: int64
              pixel0025: int64
              pixel0026: int64
              pixel0027: int64
              pixel0028: int64
              pixel0029: int64
              pixel0030: int64
              pixel0031: int64
              pixel0032: int64
              pixel0033: int64
              pixel0034: int64
              pixel0035: int64
              pixel0036: int64
              pixel0037: int64
              pixel0038: int64
              pixel0039: int64
              pixel0040: int64
              pixel0041: int64
              pixel0042: int64
              pixel0043: int64
              pixel0044: int64
              pixel0045: int64
              pixel0046: int64
              pixel0047: int64
              pixel0048: int64
              pixel0049: int64
              pixel0050: int64
              pixel0051: int64
              pixel0052: int64
              pixel0053: int64
              pixel0054: int64
              pixel0055: int64
              pixel0056: int64
              pixel0057: int64
              pixel0058: int64
              pixel0059: int64
              pixel0060: int64
              pixel0061: int64
              pixel0062: int64
              pixel0063: int64
              pixel0064: int64
              pixel0065: int64
              pixel0066: int64
              pixel0067: int64
              pixel0068: int64
              pixel0069: int64
              pixel0070: int64
              pixel0071: int64
              pixel0072: int64
              pixel0073: int64
              pixel0074: int64
              pixel0075: int64
              pixel0076: int64
              pixel0077: int64
              pixel0078: int64
              pixel0079: int64
              pixel0080: int64
              pixel0081: int64
              pixel0082: int64
              pixel0083: int64
              pixel0084: int64
              pixel0085: int64
              pixel0086: int64
              pixel0087: int64
              pixe
              ...
              int64
              pixel0703: int64
              pixel0704: int64
              pixel0705: int64
              pixel0706: int64
              pixel0707: int64
              pixel0708: int64
              pixel0709: int64
              pixel0710: int64
              pixel0711: int64
              pixel0712: int64
              pixel0713: int64
              pixel0714: int64
              pixel0715: int64
              pixel0716: int64
              pixel0717: int64
              pixel0718: int64
              pixel0719: int64
              pixel0720: int64
              pixel0721: int64
              pixel0722: int64
              pixel0723: int64
              pixel0724: int64
              pixel0725: int64
              pixel0726: int64
              pixel0727: int64
              pixel0728: int64
              pixel0729: int64
              pixel0730: int64
              pixel0731: int64
              pixel0732: int64
              pixel0733: int64
              pixel0734: int64
              pixel0735: int64
              pixel0736: int64
              pixel0737: int64
              pixel0738: int64
              pixel0739: int64
              pixel0740: int64
              pixel0741: int64
              pixel0742: int64
              pixel0743: int64
              pixel0744: int64
              pixel0745: int64
              pixel0746: int64
              pixel0747: int64
              pixel0748: int64
              pixel0749: int64
              pixel0750: int64
              pixel0751: int64
              pixel0752: int64
              pixel0753: int64
              pixel0754: int64
              pixel0755: int64
              pixel0756: int64
              pixel0757: int64
              pixel0758: int64
              pixel0759: int64
              pixel0760: int64
              pixel0761: int64
              pixel0762: int64
              pixel0763: int64
              pixel0764: int64
              pixel0765: int64
              pixel0766: int64
              pixel0767: int64
              pixel0768: int64
              pixel0769: int64
              pixel0770: int64
              pixel0771: int64
              pixel0772: int64
              pixel0773: int64
              pixel0774: int64
              pixel0775: int64
              pixel0776: int64
              pixel0777: int64
              pixel0778: int64
              pixel0779: int64
              pixel0780: int64
              pixel0781: int64
              pixel0782: int64
              pixel0783: int64
              label: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 90530
              to
              {'lesion_id': Value(dtype='string', id=None), 'image_id': Value(dtype='string', id=None), 'dx': Value(dtype='string', id=None), 'dx_type': Value(dtype='string', id=None), 'age': Value(dtype='float64', id=None), 'sex': Value(dtype='string', id=None), 'localization': Value(dtype='string', id=None)}
              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 1417, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1049, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1872, 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 785 new columns ({'pixel0662', 'pixel0661', 'pixel0269', 'pixel0029', 'pixel0321', 'pixel0526', 'pixel0328', 'pixel0389', 'pixel0237', 'pixel0354', 'pixel0000', 'pixel0253', 'pixel0746', 'pixel0198', 'pixel0565', 'pixel0756', 'pixel0059', 'pixel0407', 'pixel0483', 'pixel0101', 'pixel0363', 'pixel0053', 'pixel0118', 'pixel0293', 'pixel0044', 'pixel0370', 'pixel0338', 'pixel0762', 'pixel0154', 'pixel0343', 'pixel0577', 'pixel0352', 'pixel0061', 'pixel0069', 'pixel0151', 'pixel0573', 'pixel0581', 'pixel0434', 'pixel0259', 'pixel0575', 'pixel0129', 'pixel0379', 'pixel0707', 'pixel0256', 'pixel0394', 'pixel0547', 'pixel0537', 'pixel0592', 'pixel0623', 'pixel0591', 'pixel0294', 'pixel0240', 'pixel0320', 'pixel0035', 'pixel0578', 'pixel0764', 'pixel0016', 'pixel0401', 'pixel0382', 'pixel0486', 'pixel0258', 'pixel0210', 'pixel0677', 'pixel0457', 'pixel0779', 'pixel0247', 'pixel0712', 'pixel0479', 'pixel0026', 'pixel0620', 'pixel0682', 'pixel0624', 'pixel0392', 'pixel0466', 'pixel0741', 'pixel0177', 'pixel0721', 'pixel0639', 'pixel0748', 'pixel0215', 'pixel0440', 'pixel0406', 'pixel0566', 'pixel0590', 'pixel0124', 'pixel0236', 'pixel0263', 'pixel0042', 'pixel0579', 'pixel0228', 'pixel0651', 'pixel0727', 'pixel0369', 'pixel0763', 'pixel0179', 'pixel0255', 'pixel0311', 'pixel0141', 'pixel0170', 'pixel0307', 'pixel0371', 'pixel0464', 'pixel0108', 'pixel0221', 'pixel0298', 'pixel0368', 'pixel0729', 'pixel0743', 'pixel0648', 'pixel0199', 'pixel0642', 'pixel0010', 'pixel0071', 'pixel0014', 'pixel0265', 'pix
              ...
              461', 'pixel0614', 'pixel0130', 'pixel0373', 'pixel0378', 'pixel0652', 'pixel0374', 'pixel0641', 'pixel0052', 'pixel0395', 'pixel0087', 'pixel0731', 'pixel0691', 'pixel0549', 'pixel0217', 'pixel0011', 'pixel0063', 'pixel0515', 'pixel0169', 'pixel0542', 'pixel0765', 'pixel0266', 'pixel0171', 'pixel0271', 'pixel0090', 'pixel0173', 'pixel0067', 'pixel0219', 'pixel0416', 'pixel0523', 'pixel0197', 'pixel0292', 'pixel0319', 'pixel0336', 'pixel0443', 'pixel0012', 'pixel0699', 'pixel0524', 'pixel0144', 'pixel0643', 'pixel0740', 'pixel0273', 'pixel0162', 'pixel0268', 'pixel0233', 'pixel0602', 'pixel0514', 'pixel0384', 'pixel0504', 'pixel0127', 'pixel0732', 'pixel0153', 'pixel0733', 'pixel0558', 'pixel0655', 'pixel0413', 'pixel0409', 'pixel0242', 'pixel0032', 'pixel0244', 'pixel0249', 'pixel0286', 'pixel0350', 'pixel0444', 'pixel0454', 'pixel0462', 'pixel0453', 'pixel0039', 'pixel0351', 'pixel0033', 'pixel0398', 'pixel0637', 'pixel0077', 'pixel0023', 'pixel0125', 'pixel0397', 'pixel0760', 'pixel0728', 'pixel0709', 'pixel0201', 'pixel0569', 'pixel0674', 'pixel0657', 'pixel0099', 'pixel0645', 'pixel0004', 'pixel0506', 'pixel0100', 'pixel0225', 'pixel0783', 'pixel0318', 'pixel0231', 'pixel0660', 'pixel0516', 'pixel0323', 'pixel0076', 'pixel0759', 'pixel0175', 'pixel0190', 'pixel0212', 'pixel0234', 'pixel0040', 'pixel0487', 'pixel0043', 'pixel0507', 'pixel0739', 'pixel0715', 'pixel0425', 'pixel0267', 'pixel0463', 'pixel0527', 'pixel0278', 'pixel0616', 'pixel0145', 'pixel0204', 'pixel0181'}) and 7 missing columns ({'localization', 'dx', 'sex', 'lesion_id', 'age', 'image_id', 'dx_type'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/jarvisit/ham10000-skin-lesions/hmnist_28_28_L.csv (at revision 5004e634101c4773c2d59c83939fa57eb0ab1f76)
              
              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.

lesion_id
string
image_id
string
dx
string
dx_type
string
age
float64
sex
string
localization
string
HAM_0000118
ISIC_0027419
bkl
histo
80
male
scalp
HAM_0000118
ISIC_0025030
bkl
histo
80
male
scalp
HAM_0002730
ISIC_0026769
bkl
histo
80
male
scalp
HAM_0002730
ISIC_0025661
bkl
histo
80
male
scalp
HAM_0001466
ISIC_0031633
bkl
histo
75
male
ear
HAM_0001466
ISIC_0027850
bkl
histo
75
male
ear
HAM_0002761
ISIC_0029176
bkl
histo
60
male
face
HAM_0002761
ISIC_0029068
bkl
histo
60
male
face
HAM_0005132
ISIC_0025837
bkl
histo
70
female
back
HAM_0005132
ISIC_0025209
bkl
histo
70
female
back
HAM_0001396
ISIC_0025276
bkl
histo
55
female
trunk
HAM_0004234
ISIC_0029396
bkl
histo
85
female
chest
HAM_0004234
ISIC_0025984
bkl
histo
85
female
chest
HAM_0001949
ISIC_0025767
bkl
histo
70
male
trunk
HAM_0001949
ISIC_0032417
bkl
histo
70
male
trunk
HAM_0007207
ISIC_0031326
bkl
histo
65
male
back
HAM_0001601
ISIC_0025915
bkl
histo
75
male
upper extremity
HAM_0001601
ISIC_0031029
bkl
histo
75
male
upper extremity
HAM_0007571
ISIC_0029836
bkl
histo
70
male
chest
HAM_0007571
ISIC_0032129
bkl
histo
70
male
chest
HAM_0006071
ISIC_0032343
bkl
histo
70
female
face
HAM_0003301
ISIC_0025033
bkl
histo
60
male
back
HAM_0003301
ISIC_0027310
bkl
histo
60
male
back
HAM_0004884
ISIC_0032128
bkl
histo
75
male
upper extremity
HAM_0004884
ISIC_0025937
bkl
histo
75
male
upper extremity
HAM_0002521
ISIC_0027828
bkl
histo
40
male
upper extremity
HAM_0002521
ISIC_0029291
bkl
histo
40
male
upper extremity
HAM_0006574
ISIC_0030698
bkl
histo
40
male
back
HAM_0006574
ISIC_0025567
bkl
histo
40
male
back
HAM_0001480
ISIC_0031753
bkl
histo
70
male
abdomen
HAM_0001480
ISIC_0026835
bkl
histo
70
male
abdomen
HAM_0005772
ISIC_0031159
bkl
histo
60
female
face
HAM_0005772
ISIC_0031017
bkl
histo
60
female
face
HAM_0005612
ISIC_0024981
bkl
histo
80
male
scalp
HAM_0005388
ISIC_0027815
bkl
histo
80
male
chest
HAM_0000351
ISIC_0024324
bkl
histo
85
male
back
HAM_0000351
ISIC_0029559
bkl
histo
85
male
back
HAM_0003847
ISIC_0030661
bkl
histo
85
male
upper extremity
HAM_0003847
ISIC_0027053
bkl
histo
85
male
upper extremity
HAM_0003847
ISIC_0028560
bkl
histo
85
male
upper extremity
HAM_0003847
ISIC_0031650
bkl
histo
85
male
upper extremity
HAM_0000164
ISIC_0029161
bkl
histo
60
male
chest
HAM_0000164
ISIC_0026273
bkl
histo
60
male
chest
HAM_0007409
ISIC_0025076
bkl
histo
50
male
upper extremity
HAM_0007409
ISIC_0029687
bkl
histo
50
male
upper extremity
HAM_0007409
ISIC_0025642
bkl
histo
50
male
upper extremity
HAM_0002299
ISIC_0025819
bkl
histo
75
female
face
HAM_0002299
ISIC_0032013
bkl
histo
75
female
face
HAM_0007010
ISIC_0031691
bkl
histo
40
male
trunk
HAM_0007010
ISIC_0025419
bkl
histo
40
male
trunk
HAM_0003670
ISIC_0030105
bkl
histo
80
female
unknown
HAM_0007125
ISIC_0025016
bkl
histo
75
male
back
HAM_0007125
ISIC_0029147
bkl
histo
75
male
back
HAM_0001221
ISIC_0029301
bkl
histo
45
male
upper extremity
HAM_0001221
ISIC_0026637
bkl
histo
45
male
upper extremity
HAM_0001983
ISIC_0030377
bkl
histo
70
male
back
HAM_0003569
ISIC_0027960
bkl
histo
75
male
unknown
HAM_0003569
ISIC_0026955
bkl
histo
75
male
unknown
HAM_0000700
ISIC_0028052
bkl
histo
60
male
face
HAM_0000728
ISIC_0025286
bkl
histo
50
male
lower extremity
HAM_0003021
ISIC_0031468
bkl
histo
75
female
face
HAM_0003021
ISIC_0030926
bkl
histo
75
female
face
HAM_0000959
ISIC_0029288
bkl
histo
75
female
face
HAM_0000959
ISIC_0031008
bkl
histo
75
female
face
HAM_0001751
ISIC_0024698
nv
consensus
70
male
face
HAM_0004569
ISIC_0031495
bkl
histo
40
male
upper extremity
HAM_0004569
ISIC_0026104
bkl
histo
40
male
upper extremity
HAM_0004641
ISIC_0025099
bkl
histo
60
male
abdomen
HAM_0004641
ISIC_0031485
bkl
histo
60
male
abdomen
HAM_0005801
ISIC_0029413
bkl
histo
70
female
abdomen
HAM_0005801
ISIC_0029576
bkl
histo
70
female
abdomen
HAM_0004341
ISIC_0031967
bkl
histo
70
female
scalp
HAM_0004341
ISIC_0031584
bkl
histo
70
female
scalp
HAM_0000907
ISIC_0025140
bkl
histo
75
male
ear
HAM_0000907
ISIC_0025554
bkl
histo
75
male
ear
HAM_0001469
ISIC_0029289
bkl
histo
60
female
trunk
HAM_0001469
ISIC_0029912
bkl
histo
60
female
trunk
HAM_0001728
ISIC_0033539
bkl
histo
60
male
back
HAM_0003021
ISIC_0032283
bkl
histo
75
female
face
HAM_0003021
ISIC_0030005
bkl
histo
75
female
face
HAM_0000959
ISIC_0030189
bkl
histo
75
female
face
HAM_0000959
ISIC_0026532
bkl
histo
75
female
face
HAM_0001773
ISIC_0024832
bkl
histo
40
female
face
HAM_0001773
ISIC_0026958
bkl
histo
40
female
face
HAM_0002127
ISIC_0030768
bkl
histo
40
male
face
HAM_0002127
ISIC_0029837
bkl
histo
40
male
face
HAM_0002092
ISIC_0031624
bkl
histo
35
female
lower extremity
HAM_0002092
ISIC_0025510
bkl
histo
35
female
lower extremity
HAM_0005075
ISIC_0030607
bkl
histo
80
male
upper extremity
HAM_0005075
ISIC_0029060
bkl
histo
80
male
upper extremity
HAM_0002921
ISIC_0029308
bkl
histo
60
female
face
HAM_0003410
ISIC_0024635
bkl
histo
60
female
face
HAM_0003410
ISIC_0029425
bkl
histo
60
female
face
HAM_0004852
ISIC_0028774
bkl
histo
65
male
face
HAM_0004852
ISIC_0030565
bkl
histo
65
male
face
HAM_0000746
ISIC_0027023
bkl
histo
60
male
face
HAM_0001473
ISIC_0029022
bkl
histo
70
male
face
HAM_0003007
ISIC_0025388
bkl
histo
40
female
abdomen
HAM_0003007
ISIC_0028080
bkl
histo
40
female
abdomen
HAM_0002957
ISIC_0026153
bkl
histo
70
male
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Check out the documentation for more information.

HAM10000 Skin Lesion Dataset

Dataset Description

The HAM10000 dataset contains 10,015 dermatoscopic images of pigmented skin lesions.

Classes

  • akiec: Actinic Keratoses and Intraepithelial Carcinoma
  • bcc: Basal Cell Carcinoma
  • bkl: Benign Keratosis-like Lesions
  • df: Dermatofibroma
  • mel: Melanoma
  • nv: Melanocytic Nevi
  • vasc: Vascular Lesions

Usage 

from datasets import load_dataset

dataset = load_dataset("jarvisit/HAM10000-skin-lesions")
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