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MartialTerran
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MNIST_small_colab-sample_data

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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 163 new columns ({'84', '0.626', '241', '114.1', '18.1', '254.9', '163', '254.11', '0.630', '182', '7', '0.653', '0.661', '106', '0.623', '52.1', '0.613', '57', '254.25', '254', '0.639', '0.665', '133.1', '0.651', '38', '0.608', '1', '0.656', '0.638', '52', '0.597', '121', '0.619', '67.3', '203', '0.601', '0.621', '251', '0.632', '0.644', '121.1', '52.2', '205', '0.629', '0.627', '0.636', '0.618', '254.14', '198.5', '0.648', '0.591', '254.10', '0.592', '0.624', '0.662', '254.3', '0.598', '83', '254.2', '254.15', '0.643', '0.610', '254.8', '0.657', '207', '187', '115', '0.595', '75', '219', '0.633', '0.593', '0.646', '67.2', '254.24', '0.649', '0.667', '198.4', '58', '0.628', '0.600', '254.23', '0.607', '254.21', '0.634', '0.659', '0.654', '0.666', '36', '0.642', '254.19', '0.664', '0.612', '221', '133', '225', '254.1', '198.6', '35', '254.7', '60', '185', '0.622', '40', '0.637', '0.617', '198.1', '198.7', '0.647', '254.4', '227', '129', '254.26', '0.620', '0.650', '0.609', '198.3', '0.635', '83.1', '0.640', '77', '254.13', '0.658', '254.17', '0.631', '0.596', '254.22', '0.614', '254.5', '219.1', '0.603', '0.606', '140', '0.599', '254.18', '0.645', '238', '0.663', '59.1', '254.6', '0.594', '0.604', '0.616', '198.2', '0.602', '0.605', '14', '0.655', '229', '72', '242', '254.12', '0.641', '0.625', '0.611', '0.660', '9', '254.20', '254.16', '151', '62', '0.652', '0.615'}) and 163 missing columns ({'253.3', '252.15', '252.45', '252.13', '159.1', '252.2', '235', '178', '239', '184', '73', '152', '174', '246', '252.39', '252.31', '128', '177', '252.28', '143.1', '48', '252.5', '100', '92', '12.2', '16', '252.38', '11', '145', '248.1', '88', '252.19', '252.33', '82', '15', '3.1', '234', '89.2', '66.6', '12.3', '79', '252.37', '12', '249.2', '252.7', '252.3', '135.1', '2', '10', '252.29', '104', '66.1', '175', '149', '252.24', '30', '132', '249.1', '252.35', '252.11', '147', '2.1', '26', '199', '253.5', '193', '244', '252.36', '253.6', '66.8', '252.14', '252.47', '252.41', '252.21', '11.1', '135', '39', '247', '46', '252.18', '252.42', '12.1', '252.8', '29', '6', '66.5', '243', '252.12', '252.17', '32', '252.48', '180', '252.32', '252', '252.22', '252.34', '126.1', '155', '110', '100.1', '96', '98', '41', '252.40', '252.46', '252.6', '66.4', '46.1', '34', '66.3', '15.1', '252.43', '252.4', '24.1', '216', '5.1', '171.1', '66.9', '89', '179', '252.44', '49', '13', '143.2', '252.25', '252.23', '5.2', '17.1', '237', '252.9', '108', '20', '89.3', '96.1', '165', '209.1', '232', '253.2', '252.30', '171', '143', '239.1', '252.20', '252.10', '232.1', '243.1', '66.2', '183', '252.16', '233.1', '252.27', '253.4', '252.1', '89.1', '119', '228', '252.26', '253.1', '177.1', '131', '24', '66.7', '34.1'}).

This happened while the csv dataset builder was generating data using

hf://datasets/MartialTerran/MNIST_small_colab-sample_data/mnist_test.csv (at revision 1400859a572252e9395c7e35da3c9c7205038efd)

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 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, 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 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              7: int64
              0: int64
              0.1: int64
              0.2: int64
              0.3: int64
              0.4: int64
              0.5: int64
              0.6: int64
              0.7: int64
              0.8: int64
              0.9: int64
              0.10: int64
              0.11: int64
              0.12: int64
              0.13: int64
              0.14: int64
              0.15: int64
              0.16: int64
              0.17: int64
              0.18: int64
              0.19: int64
              0.20: int64
              0.21: int64
              0.22: int64
              0.23: int64
              0.24: int64
              0.25: int64
              0.26: int64
              0.27: int64
              0.28: int64
              0.29: int64
              0.30: int64
              0.31: int64
              0.32: int64
              0.33: int64
              0.34: int64
              0.35: int64
              0.36: int64
              0.37: int64
              0.38: int64
              0.39: int64
              0.40: int64
              0.41: int64
              0.42: int64
              0.43: int64
              0.44: int64
              0.45: int64
              0.46: int64
              0.47: int64
              0.48: int64
              0.49: int64
              0.50: int64
              0.51: int64
              0.52: int64
              0.53: int64
              0.54: int64
              0.55: int64
              0.56: int64
              0.57: int64
              0.58: int64
              0.59: int64
              0.60: int64
              0.61: int64
              0.62: int64
              0.63: int64
              0.64: int64
              0.65: int64
              0.66: int64
              0.67: int64
              0.68: int64
              0.69: int64
              0.70: int64
              0.71: int64
              0.72: int64
              0.73: int64
              0.74: int64
              0.75: int64
              0.76: int64
              0.77: int64
              0.78: int64
              0.79: int64
              0.80: int64
              0.81: int64
              0.82: int64
              0.83: int64
              0.84: int64
              0.85: int64
              0.86: int64
              0.87: int64
              0.88: int64
              0.89: int64
              0.90: int64
              0.91: int64
              0.92: int64
              0.93: int64
              0.94: int64
              0.95: int64
              0.96: int64
              0.97: int64
              0.98: int64
              0.99: int64
              0.100: int64
              0.101: int64
              0.102: int64
              0.103: int64
              0.104: int64
              0.105: int64
              0.106: int64
              0.107: int64
              0.108: int64
              0.109: int64
              0.110: int64
              0.111: int64
              0.112: int64
              0.113: int64
              0.114: int64
              0.115: int64
              0.116: int64
              0.117: int64
              0.118: int64
              0.119: int64
              0.120: int64
              0.121: int64
              0.122: int64
              0.12
              ...
              
              0.574: int64
              0.575: int64
              0.576: int64
              0.577: int64
              0.578: int64
              0.579: int64
              61: int64
              242: int64
              254.22: int64
              254.23: int64
              52.2: int64
              0.580: int64
              0.581: int64
              0.582: int64
              0.583: int64
              0.584: int64
              0.585: int64
              0.586: int64
              0.587: int64
              0.588: int64
              0.589: int64
              0.590: int64
              0.591: int64
              0.592: int64
              0.593: int64
              0.594: int64
              0.595: int64
              0.596: int64
              0.597: int64
              0.598: int64
              0.599: int64
              0.600: int64
              0.601: int64
              0.602: int64
              121: int64
              254.24: int64
              254.25: int64
              219.1: int64
              40: int64
              0.603: int64
              0.604: int64
              0.605: int64
              0.606: int64
              0.607: int64
              0.608: int64
              0.609: int64
              0.610: int64
              0.611: int64
              0.612: int64
              0.613: int64
              0.614: int64
              0.615: int64
              0.616: int64
              0.617: int64
              0.618: int64
              0.619: int64
              0.620: int64
              0.621: int64
              0.622: int64
              0.623: int64
              0.624: int64
              0.625: int64
              121.1: int64
              254.26: int64
              207: int64
              18.1: int64
              0.626: int64
              0.627: int64
              0.628: int64
              0.629: int64
              0.630: int64
              0.631: int64
              0.632: int64
              0.633: int64
              0.634: int64
              0.635: int64
              0.636: int64
              0.637: int64
              0.638: int64
              0.639: int64
              0.640: int64
              0.641: int64
              0.642: int64
              0.643: int64
              0.644: int64
              0.645: int64
              0.646: int64
              0.647: int64
              0.648: int64
              0.649: int64
              0.650: int64
              0.651: int64
              0.652: int64
              0.653: int64
              0.654: int64
              0.655: int64
              0.656: int64
              0.657: int64
              0.658: int64
              0.659: int64
              0.660: int64
              0.661: int64
              0.662: int64
              0.663: int64
              0.664: int64
              0.665: int64
              0.666: int64
              0.667: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 83691
              to
              {'6': Value('int64'), '0': Value('int64'), '0.1': Value('int64'), '0.2': Value('int64'), '0.3': Value('int64'), '0.4': Value('int64'), '0.5': Value('int64'), '0.6': Value('int64'), '0.7': Value('int64'), '0.8': Value('int64'), '0.9': Value('int64'), '0.10': Value('int64'), '0.11': Value('int64'), '0.12': Value('int64'), '0.13': Value('int64'), '0.14': Value('int64'), '0.15': Value('int64'), '0.16': Value('int64'), '0.17': Value('int64'), '0.18': Value('int64'), '0.19': Value('int64'), '0.20': Value('int64'), '0.21': Value('int64'), '0.22': Value('int64'), '0.23': Value('int64'), '0.24': Value('int64'), '0.25': Value('int64'), '0.26': Value('int64'), '0.27': Value('int64'), '0.28': Value('int64'), '0.29': Value('int64'), '0.30': Value('int64'), '0.31': Value('int64'), '0.32': Value('int64'), '0.33': Value('int64'), '0.34': Value('int64'), '0.35': Value('int64'), '0.36': Value('int64'), '0.37': Value('int64'), '0.38': Value('int64'), '0.39': Value('int64'), '0.40': Value('int64'), '0.41': Value('int64'), '0.42': Value('int64'), '0.43': Value('int64'), '0.44': Value('int64'), '0.45': Value('int64'), '0.46': Value('int64'), '0.47': Value('int64'), '0.48': Value('int64'), '0.49': Value('int64'), '0.50': Value('int64'), '0.51': Value('int64'), '0.52': Value('int64'), '0.53': Value('int64'), '0.54': Value('int64'), '0.55': Value('int64'), '0.56': Value('int64'), '0.57': Value('int64'), '0.58': Value('int64'), '0.59': Value('int64'), '0.60': Value('int64'), '0.61': Value('int64'), '0
              ...
               '0.531': Value('int64'), '0.532': Value('int64'), '0.533': Value('int64'), '0.534': Value('int64'), '0.535': Value('int64'), '0.536': Value('int64'), '0.537': Value('int64'), '0.538': Value('int64'), '0.539': Value('int64'), '0.540': Value('int64'), '0.541': Value('int64'), '0.542': Value('int64'), '0.543': Value('int64'), '0.544': Value('int64'), '0.545': Value('int64'), '0.546': Value('int64'), '0.547': Value('int64'), '0.548': Value('int64'), '0.549': Value('int64'), '0.550': Value('int64'), '0.551': Value('int64'), '0.552': Value('int64'), '0.553': Value('int64'), '0.554': Value('int64'), '0.555': Value('int64'), '0.556': Value('int64'), '0.557': Value('int64'), '0.558': Value('int64'), '0.559': Value('int64'), '0.560': Value('int64'), '0.561': Value('int64'), '0.562': Value('int64'), '0.563': Value('int64'), '0.564': Value('int64'), '0.565': Value('int64'), '0.566': Value('int64'), '0.567': Value('int64'), '0.568': Value('int64'), '0.569': Value('int64'), '0.570': Value('int64'), '0.571': Value('int64'), '0.572': Value('int64'), '0.573': Value('int64'), '0.574': Value('int64'), '0.575': Value('int64'), '0.576': Value('int64'), '0.577': Value('int64'), '0.578': Value('int64'), '0.579': Value('int64'), '0.580': Value('int64'), '0.581': Value('int64'), '0.582': Value('int64'), '0.583': Value('int64'), '0.584': Value('int64'), '0.585': Value('int64'), '0.586': Value('int64'), '0.587': Value('int64'), '0.588': Value('int64'), '0.589': Value('int64'), '0.590': Value('int64')}
              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 1456, 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 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, 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 1702, 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 1833, 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 163 new columns ({'84', '0.626', '241', '114.1', '18.1', '254.9', '163', '254.11', '0.630', '182', '7', '0.653', '0.661', '106', '0.623', '52.1', '0.613', '57', '254.25', '254', '0.639', '0.665', '133.1', '0.651', '38', '0.608', '1', '0.656', '0.638', '52', '0.597', '121', '0.619', '67.3', '203', '0.601', '0.621', '251', '0.632', '0.644', '121.1', '52.2', '205', '0.629', '0.627', '0.636', '0.618', '254.14', '198.5', '0.648', '0.591', '254.10', '0.592', '0.624', '0.662', '254.3', '0.598', '83', '254.2', '254.15', '0.643', '0.610', '254.8', '0.657', '207', '187', '115', '0.595', '75', '219', '0.633', '0.593', '0.646', '67.2', '254.24', '0.649', '0.667', '198.4', '58', '0.628', '0.600', '254.23', '0.607', '254.21', '0.634', '0.659', '0.654', '0.666', '36', '0.642', '254.19', '0.664', '0.612', '221', '133', '225', '254.1', '198.6', '35', '254.7', '60', '185', '0.622', '40', '0.637', '0.617', '198.1', '198.7', '0.647', '254.4', '227', '129', '254.26', '0.620', '0.650', '0.609', '198.3', '0.635', '83.1', '0.640', '77', '254.13', '0.658', '254.17', '0.631', '0.596', '254.22', '0.614', '254.5', '219.1', '0.603', '0.606', '140', '0.599', '254.18', '0.645', '238', '0.663', '59.1', '254.6', '0.594', '0.604', '0.616', '198.2', '0.602', '0.605', '14', '0.655', '229', '72', '242', '254.12', '0.641', '0.625', '0.611', '0.660', '9', '254.20', '254.16', '151', '62', '0.652', '0.615'}) and 163 missing columns ({'253.3', '252.15', '252.45', '252.13', '159.1', '252.2', '235', '178', '239', '184', '73', '152', '174', '246', '252.39', '252.31', '128', '177', '252.28', '143.1', '48', '252.5', '100', '92', '12.2', '16', '252.38', '11', '145', '248.1', '88', '252.19', '252.33', '82', '15', '3.1', '234', '89.2', '66.6', '12.3', '79', '252.37', '12', '249.2', '252.7', '252.3', '135.1', '2', '10', '252.29', '104', '66.1', '175', '149', '252.24', '30', '132', '249.1', '252.35', '252.11', '147', '2.1', '26', '199', '253.5', '193', '244', '252.36', '253.6', '66.8', '252.14', '252.47', '252.41', '252.21', '11.1', '135', '39', '247', '46', '252.18', '252.42', '12.1', '252.8', '29', '6', '66.5', '243', '252.12', '252.17', '32', '252.48', '180', '252.32', '252', '252.22', '252.34', '126.1', '155', '110', '100.1', '96', '98', '41', '252.40', '252.46', '252.6', '66.4', '46.1', '34', '66.3', '15.1', '252.43', '252.4', '24.1', '216', '5.1', '171.1', '66.9', '89', '179', '252.44', '49', '13', '143.2', '252.25', '252.23', '5.2', '17.1', '237', '252.9', '108', '20', '89.3', '96.1', '165', '209.1', '232', '253.2', '252.30', '171', '143', '239.1', '252.20', '252.10', '232.1', '243.1', '66.2', '183', '252.16', '233.1', '252.27', '253.4', '252.1', '89.1', '119', '228', '252.26', '253.1', '177.1', '131', '24', '66.7', '34.1'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/MartialTerran/MNIST_small_colab-sample_data/mnist_test.csv (at revision 1400859a572252e9395c7e35da3c9c7205038efd)
              
              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.

6
int64
0
int64
0.1
int64
0.2
int64
0.3
int64
0.4
int64
0.5
int64
0.6
int64
0.7
int64
0.8
int64
0.9
int64
0.10
int64
0.11
int64
0.12
int64
0.13
int64
0.14
int64
0.15
int64
0.16
int64
0.17
int64
0.18
int64
0.19
int64
0.20
int64
0.21
int64
0.22
int64
0.23
int64
0.24
int64
0.25
int64
0.26
int64
0.27
int64
0.28
int64
0.29
int64
0.30
int64
0.31
int64
0.32
int64
0.33
int64
0.34
int64
0.35
int64
0.36
int64
0.37
int64
0.38
int64
0.39
int64
0.40
int64
0.41
int64
0.42
int64
0.43
int64
0.44
int64
0.45
int64
0.46
int64
0.47
int64
0.48
int64
0.49
int64
0.50
int64
0.51
int64
0.52
int64
0.53
int64
0.54
int64
0.55
int64
0.56
int64
0.57
int64
0.58
int64
0.59
int64
0.60
int64
0.61
int64
0.62
int64
0.63
int64
0.64
int64
0.65
int64
0.66
int64
0.67
int64
0.68
int64
0.69
int64
0.70
int64
0.71
int64
0.72
int64
0.73
int64
0.74
int64
0.75
int64
0.76
int64
0.77
int64
0.78
int64
0.79
int64
0.80
int64
0.81
int64
0.82
int64
0.83
int64
0.84
int64
0.85
int64
0.86
int64
0.87
int64
0.88
int64
0.89
int64
0.90
int64
0.91
int64
0.92
int64
0.93
int64
0.94
int64
0.95
int64
0.96
int64
0.97
int64
0.98
int64
0.99
int64
0.100
int64
0.101
int64
0.102
int64
0.103
int64
0.104
int64
0.105
int64
0.106
int64
0.107
int64
0.108
int64
0.109
int64
0.110
int64
0.111
int64
0.112
int64
0.113
int64
0.114
int64
0.115
int64
0.116
int64
0.117
int64
0.118
int64
0.119
int64
0.120
int64
0.121
int64
24
int64
67
int64
67.1
int64
18
int64
0.122
int64
0.123
int64
0.124
int64
0.125
int64
0.126
int64
0.127
int64
0.128
int64
0.129
int64
0.130
int64
0.131
int64
0.132
int64
0.133
int64
0.134
int64
0.135
int64
0.136
int64
0.137
int64
0.138
int64
0.139
int64
0.140
int64
0.141
int64
0.142
int64
0.143
int64
0.144
int64
0.145
int64
131
int64
252
int64
252.1
int64
66
int64
0.146
int64
0.147
int64
0.148
int64
0.149
int64
0.150
int64
0.151
int64
0.152
int64
0.153
int64
0.154
int64
0.155
int64
0.156
int64
0.157
int64
0.158
int64
0.159
int64
0.160
int64
0.161
int64
0.162
int64
0.163
int64
0.164
int64
0.165
int64
0.166
int64
0.167
int64
0.168
int64
159
int64
250
int64
232
int64
30
int64
32
int64
0.169
int64
0.170
int64
0.171
int64
0.172
int64
0.173
int64
0.174
int64
0.175
int64
0.176
int64
0.177
int64
0.178
int64
0.179
int64
0.180
int64
0.181
int64
0.182
int64
0.183
int64
0.184
int64
0.185
int64
0.186
int64
0.187
int64
0.188
int64
0.189
int64
0.190
int64
15
int64
222
int64
252.2
int64
108
int64
0.191
int64
0.192
int64
0.193
int64
0.194
int64
0.195
int64
0.196
int64
0.197
int64
0.198
int64
0.199
int64
0.200
int64
0.201
int64
0.202
int64
0.203
int64
0.204
int64
0.205
int64
0.206
int64
0.207
int64
0.208
int64
0.209
int64
0.210
int64
0.211
int64
0.212
int64
0.213
int64
0.214
int64
147
int64
252.3
int64
183
int64
5
int64
0.215
int64
0.216
int64
0.217
int64
0.218
int64
0.219
int64
0.220
int64
0.221
int64
20
int64
89
int64
89.1
int64
73
int64
0.222
int64
0.223
int64
0.224
int64
0.225
int64
0.226
int64
0.227
int64
0.228
int64
0.229
int64
0.230
int64
0.231
int64
0.232
int64
0.233
int64
48
int64
247
int64
252.4
int64
159.1
int64
0.234
int64
0.235
int64
0.236
int64
0.237
int64
0.238
int64
0.239
int64
0.240
int64
79
int64
236
int64
252.5
int64
252.6
int64
249
int64
198
int64
16
int64
0.241
int64
0.242
int64
0.243
int64
0.244
int64
0.245
int64
0.246
int64
0.247
int64
0.248
int64
0.249
int64
41
int64
193
int64
252.7
int64
199
int64
22
int64
0.250
int64
0.251
int64
0.252
int64
0.253
int64
0.254
int64
12
int64
135
int64
248
int64
252.8
int64
252.9
int64
252.10
int64
252.11
int64
252.12
int64
100
int64
0.255
int64
0.256
int64
0.257
int64
0.258
int64
0.259
int64
0.260
int64
0.261
int64
0.262
int64
0.263
int64
100.1
int64
252.13
int64
252.14
int64
88
int64
0.264
int64
0.265
int64
0.266
int64
0.267
int64
0.268
int64
11
int64
171
int64
252.15
int64
252.16
int64
235
int64
175
int64
178
int64
252.17
int64
252.18
int64
224
int64
0.269
int64
0.270
int64
0.271
int64
0.272
int64
0.273
int64
0.274
int64
0.275
int64
0.276
int64
15.1
int64
209
int64
252.19
int64
233
int64
12.1
int64
0.277
int64
0.278
int64
0.279
int64
0.280
int64
49
int64
177
int64
252.20
int64
252.21
int64
89.2
int64
26
int64
0.281
int64
2
int64
166
int64
252.22
int64
252.23
int64
0.282
int64
0.283
int64
0.284
int64
0.285
int64
0.286
int64
0.287
int64
0.288
int64
0.289
int64
96
int64
253
int64
253.1
int64
59
int64
0.290
int64
0.291
int64
0.292
int64
0.293
int64
11.1
int64
177.1
int64
255
int64
253.2
int64
92
int64
0.294
int64
0.295
int64
0.296
int64
0.297
int64
155
int64
253.3
int64
128
int64
0.298
int64
0.299
int64
0.300
int64
0.301
int64
0.302
int64
0.303
int64
0.304
int64
0.305
int64
143
int64
252.24
int64
252.25
int64
10
int64
0.306
int64
0.307
int64
0.308
int64
12.2
int64
171.1
int64
252.26
int64
216
int64
110
int64
13
int64
0.309
int64
0.310
int64
0.311
int64
3
int64
180
int64
232.1
int64
24.1
int64
0.312
int64
0.313
int64
0.314
int64
0.315
int64
0.316
int64
0.317
int64
0.318
int64
0.319
int64
143.1
int64
252.27
int64
170
int64
2.1
int64
0.320
int64
0.321
int64
0.322
int64
135.1
int64
252.28
int64
209.1
int64
19
int64
0.323
int64
0.324
int64
0.325
int64
0.326
int64
0.327
int64
12.3
int64
252.29
int64
132
int64
0.328
int64
0.329
int64
0.330
int64
0.331
int64
0.332
int64
0.333
int64
0.334
int64
0.335
int64
0.336
int64
249.1
int64
252.30
int64
96.1
int64
0.337
int64
0.338
int64
0.339
int64
21
int64
248.1
int64
246
int64
34
int64
0.340
int64
0.341
int64
0.342
int64
0.343
int64
5.1
int64
61
int64
234
int64
152
int64
3.1
int64
0.344
int64
0.345
int64
0.346
int64
0.347
int64
0.348
int64
0.349
int64
0.350
int64
0.351
int64
0.352
int64
253.4
int64
252.31
int64
44
int64
0.353
int64
0.354
int64
0.355
int64
145
int64
252.32
int64
104
int64
0.356
int64
0.357
int64
0.358
int64
46
int64
114
int64
184
int64
252.33
int64
149
int64
34.1
int64
0.359
int64
0.360
int64
0.361
int64
0.362
int64
0.363
int64
0.364
int64
0.365
int64
0.366
int64
0.367
int64
0.368
int64
253.5
int64
252.34
int64
82
int64
0.369
int64
0.370
int64
31
int64
239
int64
252.35
int64
66.1
int64
39
int64
89.3
int64
165
int64
243
int64
252.36
int64
233.1
int64
126
int64
5.2
int64
0.371
int64
0.372
int64
0.373
int64
0.374
int64
0.375
int64
0.376
int64
0.377
int64
0.378
int64
0.379
int64
0.380
int64
0.381
int64
249.2
int64
252.37
int64
244
int64
126.1
int64
98
int64
143.2
int64
252.38
int64
252.39
int64
237
int64
240
int64
253.6
int64
252.40
int64
243.1
int64
174
int64
17
int64
0.382
int64
0.383
int64
0.384
int64
0.385
int64
0.386
int64
0.387
int64
0.388
int64
0.389
int64
0.390
int64
0.391
int64
0.392
int64
0.393
int64
0.394
int64
119
int64
239.1
int64
252.41
int64
252.42
int64
252.43
int64
252.44
int64
252.45
int64
252.46
int64
252.47
int64
252.48
int64
228
int64
179
int64
17.1
int64
0.395
int64
0.396
int64
0.397
int64
0.398
int64
0.399
int64
0.400
int64
0.401
int64
0.402
int64
0.403
int64
0.404
int64
0.405
int64
0.406
int64
0.407
int64
0.408
int64
0.409
int64
0.410
int64
46.1
int64
66.2
int64
66.3
int64
66.4
int64
66.5
int64
66.6
int64
66.7
int64
66.8
int64
66.9
int64
29
int64
0.411
int64
0.412
int64
0.413
int64
0.414
int64
0.415
int64
0.416
int64
0.417
int64
0.418
int64
0.419
int64
0.420
int64
0.421
int64
0.422
int64
0.423
int64
0.424
int64
0.425
int64
0.426
int64
0.427
int64
0.428
int64
0.429
int64
0.430
int64
0.431
int64
0.432
int64
0.433
int64
0.434
int64
0.435
int64
0.436
int64
0.437
int64
0.438
int64
0.439
int64
0.440
int64
0.441
int64
0.442
int64
0.443
int64
0.444
int64
0.445
int64
0.446
int64
0.447
int64
0.448
int64
0.449
int64
0.450
int64
0.451
int64
0.452
int64
0.453
int64
0.454
int64
0.455
int64
0.456
int64
0.457
int64
0.458
int64
0.459
int64
0.460
int64
0.461
int64
0.462
int64
0.463
int64
0.464
int64
0.465
int64
0.466
int64
0.467
int64
0.468
int64
0.469
int64
0.470
int64
0.471
int64
0.472
int64
0.473
int64
0.474
int64
0.475
int64
0.476
int64
0.477
int64
0.478
int64
0.479
int64
0.480
int64
0.481
int64
0.482
int64
0.483
int64
0.484
int64
0.485
int64
0.486
int64
0.487
int64
0.488
int64
0.489
int64
0.490
int64
0.491
int64
0.492
int64
0.493
int64
0.494
int64
0.495
int64
0.496
int64
0.497
int64
0.498
int64
0.499
int64
0.500
int64
0.501
int64
0.502
int64
0.503
int64
0.504
int64
0.505
int64
0.506
int64
0.507
int64
0.508
int64
0.509
int64
0.510
int64
0.511
int64
0.512
int64
0.513
int64
0.514
int64
0.515
int64
0.516
int64
0.517
int64
0.518
int64
0.519
int64
0.520
int64
0.521
int64
0.522
int64
0.523
int64
0.524
int64
0.525
int64
0.526
int64
0.527
int64
0.528
int64
0.529
int64
0.530
int64
0.531
int64
0.532
int64
0.533
int64
0.534
int64
0.535
int64
0.536
int64
0.537
int64
0.538
int64
0.539
int64
0.540
int64
0.541
int64
0.542
int64
0.543
int64
0.544
int64
0.545
int64
0.546
int64
0.547
int64
0.548
int64
0.549
int64
0.550
int64
0.551
int64
0.552
int64
0.553
int64
0.554
int64
0.555
int64
0.556
int64
0.557
int64
0.558
int64
0.559
int64
0.560
int64
0.561
int64
0.562
int64
0.563
int64
0.564
int64
0.565
int64
0.566
int64
0.567
int64
0.568
int64
0.569
int64
0.570
int64
0.571
int64
0.572
int64
0.573
int64
0.574
int64
0.575
int64
0.576
int64
0.577
int64
0.578
int64
0.579
int64
0.580
int64
0.581
int64
0.582
int64
0.583
int64
0.584
int64
0.585
int64
0.586
int64
0.587
int64
0.588
int64
0.589
int64
0.590
int64
5
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End of preview.
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

The purpose of this copy of the MNIST small dataset [mnist_test.csv (20,000 samples) and mnist_train_small.csv (10,000 samples)] copied from sample_data folder in Google Colab is simply to illustrate how WEIRD and totally deformed/unrecognizable are the 1% to 2% test samples that are difficult for a competent Vision model to correctly classify. See for yourself (up to 4 misclassified test samples shown per training epoch)

Vision_model_V2.1.py

--- Hyperparameters ---

[INFO] Loading datasets... [AUGMENT] Creating augmented training data... [AUGMENT] Created augmentation pipeline: [RandomAffine(degrees=[-8.0, 8.0], translate=(0.07142857142857142, 0), shear=[0.0, 0.0])] [AUGMENT] Original train size: 20000. New size: 40000 [INFO] Calculating class distribution and entropy...

--- Dataset Information --- Name: MNIST (Small) Source: Included in Google Colab's /sample_data directory Original Train Samples 20000 Total Train Samples (w/ Aug) 40000 Test Samples 10000 Image Dimensions 1x28x28 Classes [np.int64(0), np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5), np.int64(6), np.int64(7), np.int64(8), np.int64(9)] Class Entropy Contrib: Class 0: 0.3286 Class 1: 0.3540 Class 2: 0.3312 Class 3: 0.3342 Class 4: 0.3249 Class 5: 0.3087 Class 6: 0.3358 Class 7: 0.3438 Class 8: 0.3238 Class 9: 0.3343 Total Label Entropy: 3.3192 (Max: 3.3219)

--- Starting Training --- [MODEL] New best accuracy: 97.83%. Saving model to output/best_model.pt Epoch [01/20] | Train Loss: 0.1843, Train Acc: 94.28% | Test Loss: 0.0667, Test Acc: 97.83% | LR: 1.00e-03 [ANALYSIS] Displaying up to 4 failed test samples for Epoch 1... image/png [PLOT] Saved failed samples plot to output/failed_samples_epoch_1.png

[MODEL] New best accuracy: 98.10%. Saving model to output/best_model.pt Epoch [02/20] | Train Loss: 0.0830, Train Acc: 97.45% | Test Loss: 0.0570, Test Acc: 98.10% | LR: 1.00e-03 [ANALYSIS] Displaying up to 4 failed test samples for Epoch 2... image/png [PLOT] Saved failed samples plot to output/failed_samples_epoch_2.png

[MODEL] New best accuracy: 98.29%. Saving model to output/best_model.pt Epoch [03/20] | Train Loss: 0.0589, Train Acc: 98.16% | Test Loss: 0.0529, Test Acc: 98.29% | LR: 1.00e-03 [ANALYSIS] Displaying up to 4 failed test samples for Epoch 3... image/png [PLOT] Saved failed samples plot to output/failed_samples_epoch_3.png

[MODEL] New best accuracy: 98.72%. Saving model to output/best_model.pt Epoch [04/20] | Train Loss: 0.0473, Train Acc: 98.51% | Test Loss: 0.0426, Test Acc: 98.72% | LR: 1.00e-03 [ANALYSIS] Displaying up to 4 failed test samples for Epoch 4... image/png [PLOT] Saved failed samples plot to output/failed_samples_epoch_4.png

Epoch [05/20] | Train Loss: 0.0398, Train Acc: 98.71% | Test Loss: 0.0470, Test Acc: 98.54% | LR: 1.00e-03 [ANALYSIS] Displaying up to 4 failed test samples for Epoch 5... image/png [PLOT] Saved failed samples plot to output/failed_samples_epoch_5.png

[MODEL] New best accuracy: 98.75%. Saving model to output/best_model.pt Epoch [06/20] | Train Loss: 0.0374, Train Acc: 98.79% | Test Loss: 0.0400, Test Acc: 98.75% | LR: 1.00e-03 [ANALYSIS] Displaying up to 4 failed test samples for Epoch 6... image/png [PLOT] Saved failed samples plot to output/failed_samples_epoch_6.png

Confusion Matrix: image/png

Training Telemetry Plot: image/png

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