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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 18961 new columns ({'176.23', '113].1', '146.41', '183.47', '182.33', '200.30', '41.30', '199.41', '114.43', '209.7', '103.40', '66.30', '{"id":164', '150.8', '6.7', '167.57', '171.47', '166.6', 'iscrowd:0.253', '144.68', '53.28', '101.47', '36]].1', '137.78', '124.55', '58.23', '55.12', 'date_captured:""}.113', '46.28', '73.60', '184.32', '52.22', '180.1', 'width:224.288', '92.57', '{"id":260', '{"segmentation":[[137.1', 'file_name:"298.jpg"', '165.54', '75.38', '186.49', '57.16', '109.48', '191.16', 'file_name:"182.jpg"', '38.13', 'iscrowd:0.262', '124.10', 'date_captured:""}.65', '200.47', '97]]', '54.8', 'bbox:[136', '{"segmentation":[[35', 'height:224.76', '81.55', 'id:115', '{"segmentation":[[58.1', 'license:0.82', '120.33', '213.7', '125.23', '86.8', '224.17', '107.16', '106.4', '76.41', '28.13', '26.9', '99.2', '80.23', 'height:224.250', '55.3', '166.13', '157.20', '186.25', '15.26', 'area:3468', '21.23', '14.2', '110.8', '96.38', '135].1', '1]]', '145.70', '190]]', '73.33', '86.95', '28.12', '81.74', '152.8', '71.22', '107.50', '153.58', '31.36', '134.65', '140.18', '127.75', '82]].1', '38.39', '206.6', '1.60', 'date_captured:""}.24', '219.36', '120.20', 'file_name:"66.jpg"', '8.13', '100.18', '134.14', 'height:224.33', '83.64', 'image_id:249', '103.74', '137.76', '219.37', '66.24', '87.38', '177.46', '148.61', '{"segmentation":[[0.4', '44].2', '12].2', '29.22', '{"id":29', '65.37', '63].1', '119.21', 'license:0.210', 'date_captured:""}.119', 'bbox:[4.1', '223.7', '30].2', '173.26', 'i
...
142.46', '77]]', 'bbox:[1.13', '49.53', '129.23', 'id:130', '168.37', '191.39', '221.4', 'iscrowd:0.75', '23.9', '62.23', '82.32', '86.73', '46.26', 'height:224.38', '47.36', '49.11', '223.26', 'width:224.99', '223.15', '190.6', '211.16', 'date_captured:""}.186', '223.148', '158.46', '33.16', 'image_id:228.3', '161.63', '{"segmentation":[[143.1', '138.51', 'area:14229', '124.57', 'date_captured:""}.33', '172.16', '93.56', 'date_captured:""}.131', 'image_id:59', '{"segmentation":[[68.2', '92.13', '1.22', '91.11', '223.84', '155.53', '156.9', 'category_id:null}.309', 'bbox:[50', '100.4', '36', '208.28', 'id:317', '102.7', '170.45', '171.53', '{"segmentation":[[40.1', 'iscrowd:0.334', '56.2', '80.74', '178.19', '73.44', '124.74', '188.14', '146.10', '218.10', '109.62', '113.39', '117.60', '168.25', '60.40', '223.178', '11].1', '156.24', '130.38', '67.49', '38.30', 'area:2964', 'date_captured:""}.58', '99.44', '108.36', '176.52', 'width:224.262', '69.61', '119.3', '6.6', '103.9', '165.40', '101.74', '189.28', '100.12', '81.26', '157.22', 'license:0.262', '76.71', '95]]', '129.21', '152.62', '101.16', '33.26', '174.63', '215.19', 'iscrowd:0.105', '64.12', '130.9', 'id:380', '78.30', '72.34', '63].3', 'bbox:[179', '49.37', '100.6', 'date_captured:""}.181', 'id:61', '59.26', 'iscrowd:0.247', '32.37', '155.18', '223.40', '77.37', '131.62', 'image_id:121.5', '203.28', '162.57', '22.6', '104.26', '163.27', '106.50', '213.27', '139.73', '63.39', '{"id":139', '141.79', '78.13', '126.78'}) and 2 missing columns ({' Object', 'Index'}).

This happened while the csv dataset builder was generating data using

hf://datasets/IVUlab/pixmmvp/Segmentations.json (at revision 4cbcd2917ea9a2fba8cce1c5ab7d3da0f0ea7e2e)

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
              {"info":{"year":2024: null
              version:"1.0": null
              description:"VIA project exported to COCO format using VGG Image Annotator (http://www.robots.ox.ac.uk/~vgg/software/via/)": null
              contributor:"": null
              url:"http://www.robots.ox.ac.uk/~vgg/software/via/": null
              date_created:"Sat Dec 07 2024 09:04:59 GMT-0500 (Eastern Standard Time)"}: null
              images:[{"id":1: null
              width:224: null
              height:224: null
              file_name:"1.jpg": null
              license:0: null
              date_captured:""}: null
              {"id":2: null
              width:224.1: null
              height:224.1: null
              file_name:"2.jpg": null
              license:0.1: null
              date_captured:""}.1: null
              {"id":3: null
              width:224.2: null
              height:224.2: null
              file_name:"3.jpg": null
              license:0.2: null
              date_captured:""}.2: null
              {"id":4: null
              width:224.3: null
              height:224.3: null
              file_name:"4.jpg": null
              license:0.3: null
              date_captured:""}.3: null
              {"id":5: null
              width:224.4: null
              height:224.4: null
              file_name:"5.jpg": null
              license:0.4: null
              date_captured:""}.4: null
              {"id":6: null
              width:224.5: null
              height:224.5: null
              file_name:"6.jpg": null
              license:0.5: null
              date_captured:""}.5: null
              {"id":7: null
              width:224.6: null
              height:224.6: null
              file_name:"7.jpg": null
              license:0.6: null
              date_captured:""}.6: null
              {"id":8: null
              width:224.7: null
              height:224.7: null
              file_name:"8.jpg": null
              license:0.7: null
              date_captured:""}.7: null
              {"id":9: null
              width:224.8: null
              height:224.8: null
              file_name:"9.jpg": null
              license:0.8: null
              date_captured:""}.8: null
              {"id":10: null
              width:224.9: null
              height:224.9: null
              file_name:"10.jpg": null
              license:0.9: nul
              ...
              29: null
              66.73: null
              222.87: null
              138.93: null
              214.31: null
              143.82: null
              222.88: null
              158.64: null
              220.30: null
              173.67: null
              220.31: null
              188.42: null
              216.37: null
              197.35: null
              211.33: null
              207.33: null
              201.42: null
              211.34: null
              188.43: null
              216.38: null
              173.68: null
              216.39: null
              161.63: null
              210.44: null
              153.58: null
              200.53: null
              146.64: null
              214.32: null
              128.101: null
              222.89: null
              105.86: null
              222.90: null
              75.69: null
              217.27: null
              61.52: null
              200.54: null
              54.44: null
              184.50: null
              53.67: null
              173.69: null
              45.43: null
              187.61: null
              34.55: null
              192.46: null
              16.22: null
              192.47: null
              1.107: null
              185.48: null
              2.46: null
              126.96: null
              6.21: null
              115.77: null
              19.23: null
              111.84: null
              25.27: null
              108.76: null
              24.30: null
              99.52: null
              19.24: null
              80.86: null
              19.25: null
              68.79: null
              28.31: null
              54.45: null
              39.36: null
              45.44: null
              59.65: null
              43.48: null
              70.81: null
              44]].1: null
              area:42370: null
              bbox:[1.36: null
              32.62: null
              223.183: null
              190].1: null
              iscrowd:0.383: null
              id:384: null
              image_id:298: null
              category_id:null}]: null
              licenses:[{"id":0: null
              name:"Unknown License": null
              url:""}]: null
              categories:[{"supercategory":"type": null
              id:null: null
              name:"Bird"}: null
              {"supercategory":"type": null
              id:null.1: null
              name:"Human"}: null
              {"supercategory":"type".1: null
              id:null.2: null
              name:"Cup (object)"}: null
              {"supercategory":"type".2: null
              id:null.3: null
              name:"Unknown (object)"}]}: null
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2142241
              to
              {'Index': Value(dtype='int64', id=None), ' Object': 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 1438, 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 1050, 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 18961 new columns ({'176.23', '113].1', '146.41', '183.47', '182.33', '200.30', '41.30', '199.41', '114.43', '209.7', '103.40', '66.30', '{"id":164', '150.8', '6.7', '167.57', '171.47', '166.6', 'iscrowd:0.253', '144.68', '53.28', '101.47', '36]].1', '137.78', '124.55', '58.23', '55.12', 'date_captured:""}.113', '46.28', '73.60', '184.32', '52.22', '180.1', 'width:224.288', '92.57', '{"id":260', '{"segmentation":[[137.1', 'file_name:"298.jpg"', '165.54', '75.38', '186.49', '57.16', '109.48', '191.16', 'file_name:"182.jpg"', '38.13', 'iscrowd:0.262', '124.10', 'date_captured:""}.65', '200.47', '97]]', '54.8', 'bbox:[136', '{"segmentation":[[35', 'height:224.76', '81.55', 'id:115', '{"segmentation":[[58.1', 'license:0.82', '120.33', '213.7', '125.23', '86.8', '224.17', '107.16', '106.4', '76.41', '28.13', '26.9', '99.2', '80.23', 'height:224.250', '55.3', '166.13', '157.20', '186.25', '15.26', 'area:3468', '21.23', '14.2', '110.8', '96.38', '135].1', '1]]', '145.70', '190]]', '73.33', '86.95', '28.12', '81.74', '152.8', '71.22', '107.50', '153.58', '31.36', '134.65', '140.18', '127.75', '82]].1', '38.39', '206.6', '1.60', 'date_captured:""}.24', '219.36', '120.20', 'file_name:"66.jpg"', '8.13', '100.18', '134.14', 'height:224.33', '83.64', 'image_id:249', '103.74', '137.76', '219.37', '66.24', '87.38', '177.46', '148.61', '{"segmentation":[[0.4', '44].2', '12].2', '29.22', '{"id":29', '65.37', '63].1', '119.21', 'license:0.210', 'date_captured:""}.119', 'bbox:[4.1', '223.7', '30].2', '173.26', 'i
              ...
              142.46', '77]]', 'bbox:[1.13', '49.53', '129.23', 'id:130', '168.37', '191.39', '221.4', 'iscrowd:0.75', '23.9', '62.23', '82.32', '86.73', '46.26', 'height:224.38', '47.36', '49.11', '223.26', 'width:224.99', '223.15', '190.6', '211.16', 'date_captured:""}.186', '223.148', '158.46', '33.16', 'image_id:228.3', '161.63', '{"segmentation":[[143.1', '138.51', 'area:14229', '124.57', 'date_captured:""}.33', '172.16', '93.56', 'date_captured:""}.131', 'image_id:59', '{"segmentation":[[68.2', '92.13', '1.22', '91.11', '223.84', '155.53', '156.9', 'category_id:null}.309', 'bbox:[50', '100.4', '36', '208.28', 'id:317', '102.7', '170.45', '171.53', '{"segmentation":[[40.1', 'iscrowd:0.334', '56.2', '80.74', '178.19', '73.44', '124.74', '188.14', '146.10', '218.10', '109.62', '113.39', '117.60', '168.25', '60.40', '223.178', '11].1', '156.24', '130.38', '67.49', '38.30', 'area:2964', 'date_captured:""}.58', '99.44', '108.36', '176.52', 'width:224.262', '69.61', '119.3', '6.6', '103.9', '165.40', '101.74', '189.28', '100.12', '81.26', '157.22', 'license:0.262', '76.71', '95]]', '129.21', '152.62', '101.16', '33.26', '174.63', '215.19', 'iscrowd:0.105', '64.12', '130.9', 'id:380', '78.30', '72.34', '63].3', 'bbox:[179', '49.37', '100.6', 'date_captured:""}.181', 'id:61', '59.26', 'iscrowd:0.247', '32.37', '155.18', '223.40', '77.37', '131.62', 'image_id:121.5', '203.28', '162.57', '22.6', '104.26', '163.27', '106.50', '213.27', '139.73', '63.39', '{"id":139', '141.79', '78.13', '126.78'}) and 2 missing columns ({' Object', 'Index'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/IVUlab/pixmmvp/Segmentations.json (at revision 4cbcd2917ea9a2fba8cce1c5ab7d3da0f0ea7e2e)
              
              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.

Index
int64
Object
string
1
the butterfly's wings
2
the butterfly's wings
3
the flame of the match
4
the flame of the match
5
the dog's face
6
the dog's face
7
the school bus
8
the school bus
9
the key ""Z""
10
the key ""Z""
11
the peacock's tail
12
the peacock's tail
13
an ear of corn
14
an ear of corn
15
a shadow on the flower
16
a shadow on the flower
17
the front of the school bus
18
the front of the school bus
19
the dorsal fin of the animal
20
the dorsal fin of the animal
21
the duck's entire beak
22
the duck's entire beak
23
None
24
None
25
the chicken's body
26
the chicken's body
27
the peacock's head
28
the peacock's head
29
the window on the school bus
30
the window on the school bus
31
the flag
32
the flag
33
the butterfly's abdomen
34
the butterfly's abdomen
35
the vegetables with spikes
36
the vegetables with spikes
37
the flowers in the background
38
the flowers in the background
39
the duck
40
the duck
41
the spider's legs
42
the spider's legs
43
the wheels of the school bus
44
the wheels of the school bus
45
the shark's belly
46
the shark's belly
47
the spots on the animal
48
the spots on the animal
49
the arrow keys
50
the arrow keys
51
the elephant's trunk
52
the elephant's trunk
53
the pills
54
the pills
55
the stems of bananas
56
the stems of bananas
57
the crocodile
58
the crocodile
59
the lock
60
the lock
61
the letter ""J""
62
the letter ""J""
63
one daisy that is under the shadow of a taller daisy
64
one daisy that is under the shadow of a taller daisy
65
the letter D
66
the letter D
67
the clouds
68
the clouds
69
the snake's tongue
70
the snake's tongue
71
the lock
72
the lock
73
the ground
74
the ground
75
the flower
76
the flower
77
the caudal fin of the shark
78
the caudal fin of the shark
79
the snake's head
80
the snake's head
81
a hammerhead shark
82
a hammerhead shark
83
the door of the truck cab
84
the door of the truck cab
85
the ears of the dog
86
the ears of the dog
87
a hand using the mouse
88
a hand using the mouse
89
people
90
people
91
words on the vehicle's lightbar
92
words on the vehicle's lightbar
93
accessory on the wrists
94
accessory on the wrists
95
the spider web
96
the spider web
97
the keyboard
98
the keyboard
99
the elephant's tusks
100
the elephant's tusks
End of preview.

PixMMVP Benchmark

The dataset annotations augmenting MMVP with referring expressions and corresponding segmentation masks for the objects of interest in their respective questions within the original VQA task.

Acknowledgements

I acknowledge the use of MMVP dataset's images and questions/choices part of building this dataset, the original MMVP.

References

Please city my work if you find the dataset useful

@article{siam2025pixfoundation,
  title={PixFoundation: Are We Heading in the Right Direction with Pixel-level Vision Foundation Models?},
  author={Siam, Mennatullah},
  journal={arXiv preprint arXiv:2502.04192},
  year={2025}
}
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