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The dataset generation failed
Error code: DatasetGenerationError
Exception: UnicodeDecodeError
Message: 'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1997, in _prepare_split_single
for _, table in generator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/text/text.py", line 85, in _generate_tables
batch = f.read(self.config.chunksize)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1104, in read_with_retries
out = read(*args, **kwargs)
File "/usr/local/lib/python3.9/codecs.py", line 322, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1396, 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 1045, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, 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 1884, 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 2040, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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Pedestrian 0 0 -0.24805212020874023 733.77 168.24 791.59 284.86 0.6100000143051147 1.7200000286102295 0.7300000190734863 1.7560619115829468 1.1315209865570068 7.883908748626709 6.25 0.3400000035762787 |
Pedestrian 0 0 0.02304983139038086 731.04 168.59 789.75 283.53 0.5899999737739563 1.6799999475479126 0.8399999737739563 1.7413666248321533 1.1021308898925781 7.861866474151611 6.519999980926514 0.27000001072883606 |
Pedestrian 0 0 -0.19518041610717773 733.69 171.52 790.52 285.3 0.6200000047683716 1.690000057220459 0.7400000095367432 1.7634093761444092 1.1388684511184692 7.942689895629883 6.309999942779541 0.23999999463558197 |
Pedestrian 0 0 -0.18184709548950195 734.02 166.82 790.27 286.28 0.5899999737739563 1.7000000476837158 0.7099999785423279 1.6899338960647583 1.1021308898925781 7.6120500564575195 6.320000171661377 0.23000000417232513 |
Pedestrian 0 0 0.002185821533203125 732.52 167.1 789.66 281.34 0.5899999737739563 1.690000057220459 0.8299999833106995 1.7707569599151611 1.0947833061218262 7.979426860809326 6.5 0.20999999344348907 |
Pedestrian 0 0 -0.16233301162719727 735.03 166.21 790.22 284.33 0.6000000238418579 1.7000000476837158 0.7099999785423279 1.719323992729187 1.0947833061218262 7.707568168640137 6.340000152587891 0.20000000298023224 |
Pedestrian 0 0 -0.24811792373657227 733.62 168.21 791.46 284.87 0.6100000143051147 1.7200000286102295 0.7300000190734863 1.7560619115829468 1.1315209865570068 7.883908748626709 6.25 0.3400000035762787 |
Pedestrian 0 0 0.024756431579589844 730.85 168.54 789.55 283.48 0.5899999737739563 1.6799999475479126 0.8399999737739563 1.7413666248321533 1.1021308898925781 7.861866474151611 6.53000020980835 0.27000001072883606 |
Pedestrian 0 0 -0.1952071189880371 733.47 171.4 790.31 285.2 0.6200000047683716 1.690000057220459 0.7400000095367432 1.7634093761444092 1.1388684511184692 7.935342311859131 6.309999942779541 0.23999999463558197 |
Pedestrian 0 0 -0.18215370178222656 734.04 166.68 790.3 286.15 0.5899999737739563 1.7000000476837158 0.7099999785423279 1.6899338960647583 1.1021308898925781 7.6120500564575195 6.320000171661377 0.23000000417232513 |
Pedestrian 0 0 0.003184795379638672 732.5 167.0 789.66 281.28 0.5899999737739563 1.690000057220459 0.8299999833106995 1.7707569599151611 1.0947833061218262 7.979426860809326 6.5 0.20999999344348907 |
Pedestrian 0 0 -0.16290855407714844 735.21 166.2 790.44 284.35 0.6000000238418579 1.7000000476837158 0.7099999785423279 1.719323992729187 1.0947833061218262 7.707568168640137 6.340000152587891 0.20000000298023224 |
Pedestrian 0 0 -0.24830389022827148 712.56 73.38 816.33 287.44 0.6100000143051147 1.7200000286102295 0.7300000190734863 1.340000033378601 0.8600000143051147 5.989999771118164 6.25 0.3400000035762787 |
Pedestrian 0 0 0.024805545806884766 706.71 74.45 814.19 286.63 0.5899999737739563 1.6799999475479126 0.8399999737739563 1.3200000524520874 0.8399999737739563 5.980000019073486 6.53000020980835 0.27000001072883606 |
Pedestrian 0 0 -0.19546747207641602 712.96 79.05 814.97 287.89 0.6200000047683716 1.690000057220459 0.7400000095367432 1.340000033378601 0.8700000047683716 6.03000020980835 6.309999942779541 0.23999999463558197 |
Pedestrian 0 0 -0.18210458755493164 713.51 69.62 814.48 288.91 0.5899999737739563 1.7000000476837158 0.7099999785423279 1.2899999618530273 0.8399999737739563 5.78000020980835 6.320000171661377 0.23000000417232513 |
Pedestrian 0 0 0.003154754638671875 709.28 73.51 813.94 284.26 0.5899999737739563 1.690000057220459 0.8299999833106995 1.340000033378601 0.8299999833106995 6.059999942779541 6.5 0.20999999344348907 |
Pedestrian 0 0 -0.1626124382019043 714.93 70.03 814.01 286.96 0.6000000238418579 1.7000000476837158 0.7099999785423279 1.309999942779541 0.8299999833106995 5.860000133514404 6.340000152587891 0.20000000298023224 |
Bus 0 0 -1.6105599403381348 603.57 161.23 626.52 184.12 2.7799999713897705 3.0299999713897705 10.739999771118164 0.49963265657424927 1.0947833061218262 74.1440200805664 4.679999828338623 0.1599999964237213 |
Bus 0 0 -1.610457420349121 603.49 161.09 626.42 183.97 2.7799999713897705 3.0299999713897705 10.739999771118164 0.4922851324081421 1.0800882577896118 74.16606140136719 4.679999828338623 0.1599999964237213 |
Bus 0 0 -1.6104674339294434 595.64 141.59 636.96 184.49 2.7799999713897705 3.0299999713897705 10.739999771118164 0.3700000047683716 0.8199999928474426 56.349998474121094 4.679999828338623 0.1599999964237213 |
Car 0 0 -1.552112102508545 667.47 195.47 693.74 219.15 1.9299999475479126 1.600000023841858 4.5 4.055841445922852 2.5128583908081055 40.9257926940918 4.829999923706055 0.2800000011920929 |
Car 0 0 -1.5312614440917969 666.88 194.51 694.5 219.01 1.9199999570846558 1.649999976158142 4.5 4.011756420135498 2.4761204719543457 40.404117584228516 4.849999904632568 0.1899999976158142 |
Car 0 0 -1.5525717735290527 667.83 195.44 694.08 219.13 1.9299999475479126 1.600000023841858 4.5 4.077884197235107 2.5128583908081055 40.91844177246094 4.829999923706055 0.2800000011920929 |
Car 0 0 -1.5313997268676758 666.92 194.45 694.52 218.95 1.9199999570846558 1.649999976158142 4.5 4.011756420135498 2.4687728881835938 40.41146469116211 4.849999904632568 0.1899999976158142 |
Car 0 0 -1.5525259971618652 656.26 179.45 704.78 220.8 1.9299999475479126 1.600000023841858 4.5 3.0999999046325684 1.909999966621399 31.09000015258789 4.829999923706055 0.2800000011920929 |
Car 0 0 -1.5313982963562012 654.76 177.91 704.36 220.64 1.9199999570846558 1.649999976158142 4.5 3.049999952316284 1.8799999952316284 30.709999084472656 4.849999904632568 0.1899999976158142 |
Car 0 0 1.5478181838989258 622.38 196.25 710.12 278.38 1.8600000143051147 1.600000023841858 4.309999942779541 0.9625275731086731 1.6385011672973633 12.843497276306152 1.6200000047683716 0.6100000143051147 |
Car 0 0 1.5489115715026855 625.17 197.49 707.71 276.21 1.8300000429153442 1.600000023841858 4.329999923706055 1.013960361480713 1.6899338960647583 13.401910781860352 1.6200000047683716 0.23000000417232513 |
Car 0 0 1.5483198165893555 621.97 196.14 709.66 278.2 1.8600000143051147 1.600000023841858 4.309999942779541 0.9551800489425659 1.6385011672973633 12.843497276306152 1.6200000047683716 0.6100000143051147 |
Car 0 0 1.5493054389953613 625.02 197.45 707.53 276.14 1.8300000429153442 1.600000023841858 4.329999923706055 1.013960361480713 1.6899338960647583 13.409258842468262 1.6200000047683716 0.23000000417232513 |
Car 0 0 1.5475200414657593 580.86 139.59 757.79 292.08 1.8600000143051147 1.600000023841858 4.309999942779541 0.7400000095367432 1.25 9.75 1.6200000047683716 0.6100000143051147 |
Car 0 0 1.5492982864379883 586.05 144.24 751.47 288.79 1.8300000429153442 1.600000023841858 4.329999923706055 0.7699999809265137 1.2799999713897705 10.1899995803833 1.6200000047683716 0.23000000417232513 |
Motorcycle 0 0 -1.454369068145752 328.68 183.32 356.54 231.46 0.7200000286102295 1.6799999475479126 2.1600000858306885 -7.2740631103515625 1.5356355905532837 19.69875144958496 4.480000019073486 0.1599999964237213 |
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Car 0 0 -1.5855975151062012 571.06 183.4 610.29 218.52 1.9199999570846558 1.649999976158142 4.369999885559082 -0.7421014308929443 1.6385011672973633 27.55327033996582 4.670000076293945 0.27000001072883606 |
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End of preview.
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Project Website | Paper | Talk
Yuliang Guo, Abhinav Kumar, Cheng Zhao, Ruoyu Wang, Xinyu Huang, Liu Ren
Bosch Research North America, Bosch Center for AI
in ECCV 2024
This dataset includes the additional instance masks computed from maskrcnn and 3D object detection results from FCOS3D on nuScenes, KITTI, and Waymo (front-vew) datasets.
license: mit
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