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The dataset generation failed
Error code: DatasetGenerationError
Exception: IndexError
Message: list index out of range
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1859, in _prepare_split_single
original_shard_lengths[original_shard_id] += len(table)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
IndexError: list index out of range
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 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, 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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ACs |
Compressors |
Computers |
Fridges |
Laptops |
Microwave |
TV |
unsure |
5 0.37449856733524356 0.5430117796879975 0.2985673352435529 0.3952881248010186 |
1 0.42234957020057307 0.2878064310729067 0.11919770773638973 0.13957338427252466 |
6 0.37466063348416284 0.16692662176533143 0.17918552036199095 0.2350230414746544 |
5 0.47375565610859743 0.31095356256646567 0.3212669683257917 0.41942573555476753 |
1 0.7525252525252526 0.8221590909090909 0.494949494949495 0.35568181818181827 |
1 0.24444444444444444 0.6926136363636364 0.47676767676767673 0.3556818181818182 |
1 0.4393939393939394 0.3136363636363636 0.8666666666666666 0.6181818181818182 |
1 0.8060606060606061 0.29886363636363633 0.19797979797979792 0.44090909090909086 |
1 0.9585858585858585 0.24090909090909093 0.08282828282828276 0.36363636363636365 |
4 0.2629200610997964 0.12321792260692463 0.3814918533604888 0.24643584521384926 |
2 0.27494908350305497 0.32959945430095233 0.5498981670061099 0.6551255888463038 |
0 0.1345912388392857 0.36785714285714294 0.21700613839285712 0.3642857142857143 |
0 0.3213588169642857 0.3616071428571429 0.19210379464285712 0.3517857142857143 |
0 0.1719447544642857 0.5410714285714286 0.1849888392857143 0.10714285714285715 |
0 0.37057059151785715 0.639732142857143 0.27155412946428575 0.3169642857142858 |
0 0.18973214285714285 0.7863095215388707 0.3794642857142857 0.42023809977940146 |
0 0.6035853794642857 0.6875 0.31780133928571425 0.3071428571428572 |
0 0.5650460379464285 0.47142857142857136 0.27866908482142855 0.24285714285714285 |
0 0.8235560825892856 0.6723214285714286 0.1600864955357143 0.24642857142857125 |
4 0.3998217922606925 0.22657841140529528 0.31619144602851323 0.19653767820773918 |
2 0.4298943482688391 0.38645621181262724 0.4015402240325865 0.4816700610997963 |
5 0.5940987723214284 0.3370535714285714 0.1826171874999999 0.08303571428571428 |
5 0.6006208147321427 0.40892857142857153 0.148228236607143 0.06071428571428576 |
5 0.6041782924107142 0.49330357142857145 0.21226283482142855 0.11160714285714292 |
6 0.5656389508928568 0.5691964285714284 0.21344866071428586 0.1437500000000003 |
6 0.452392578125 0.5133928571428572 0.10316685267857145 0.0875 |
6 0.4482421875 0.4441964285714286 0.11858258928571423 0.07410714285714284 |
3 0.20749876563528638 0.17901785714285715 0.21047152732060567 0.14732142857142855 |
3 0.9001152073732719 0.12946428571428573 0.19976958525345623 0.22321428571428573 |
3 0.698598996050033 0.16875 0.25327929558920337 0.15892857142857145 |
3 0.4007282751810402 0.21473214285714284 0.17836570111915737 0.07767857142857139 |
3 0.1700419684002633 0.2995535714285715 0.27349407504937456 0.1526785714285714 |
3 0.11593770572745227 0.4892857142857143 0.20333689927583937 0.22499999999999992 |
3 0.3371111751152074 0.5776785714285714 0.23901003949967084 0.18035714285714277 |
3 0.3186800526662278 0.4125 0.19976958525345626 0.16071428571428575 |
3 0.9126193219223174 0.7955357142857143 0.17476135615536534 0.38750000000000007 |
3 0.6141725641869651 0.746875 0.29846527320605665 0.2276785714285716 |
3 0.6932480250164581 0.3348214285714285 0.266359447004608 0.1696428571428571 |
3 0.9114302172481896 0.3339285714285714 0.1771395655036207 0.21785714285714286 |
6 0.46612903225806446 0.2835227272727273 0.5478529534981148 0.3693181818181819 |
4 0.45714285714285713 0.4441964285714286 0.30793650793650795 0.53125 |
4 0.592063492063492 0.5642857142857141 0.8158730158730159 0.3857142857142852 |
4 0.2015873015873016 0.6816964285714286 0.17460317460317454 0.34017857142857144 |
2 0.7801587301587299 0.799107142857143 0.4396825396825394 0.14999999999999986 |
2 0.9055524553571427 0.26205357142857166 0.1888950892857139 0.27053571428571477 |
5 0.1896003054989817 0.3844195519348268 0.22339613034623224 0.40224032586558034 |
0 0.5419224330357142 0.49374999999999986 0.8182198660714284 0.5035714285714284 |
6 0.667069206714944 0.46874999999999967 0.6658615865701116 0.6053571428571424 |
3 0.7271189927583938 0.159375 0.5457620144832126 0.3151785714285714 |
3 0.43968253968253956 0.5080357142857137 0.7746031746031743 0.7553571428571421 |
6 0.6049455915178572 0.5433035714285714 0.7901088169642857 0.9133928571428571 |
5 0.25727901131126935 0.04204545454545454 0.2451717637201508 0.07954545454545454 |
5 0.5644531250000003 0.6107954545454544 0.4648437500000002 0.30795454545454537 |
3 0.526439153369725 0.7221590909090909 0.3166209584295612 0.2920454545454546 |
5 0.375757077024358 0.4805059501102994 0.751514154048716 0.44613095692225874 |
6 0.36976207386363635 0.45852272727272736 0.6097301136363636 0.41931818181818187 |
0 0.367757645580226 0.30170454545454506 0.4994239631336403 0.5988636363636356 |
6 0.36286272321428564 0.45223214285714264 0.7257254464285713 0.7205357142857138 |
4 0.44873949579831934 0.2104166643960135 0.8974789915966387 0.40297618593488416 |
4 0.45075630252100835 0.3058035714285714 0.8907563025210083 0.3580357142857143 |
4 0.4480672268907563 0.4883928571428571 0.8907563025210083 0.41607142857142854 |
4 0.26218487394957984 0.7160714285714286 0.5243697478991597 0.2607142857142856 |
4 0.5139495798319327 0.6584821428571429 0.7253781512605041 0.2544642857142856 |
6 0.7814479638009054 0.5293112116641526 0.39547511312217215 0.8172951231774758 |
6 0.4357466063348416 0.5598793363499247 0.330316742081448 0.8012066365007543 |
6 0.46839914118139925 0.3363636363636363 0.8248062421449518 0.6318181818181817 |
4 0.23596638655462188 0.21250000000000002 0.45714285714285713 0.425 |
4 0.25176470588235295 0.2785714285714286 0.5021848739495799 0.5392857142857143 |
4 0.3347899159663866 0.39211309296744207 0.6695798319327732 0.7735119002205983 |
4 0.3146218487394958 0.6361607142857143 0.6211764705882353 0.48660714285714285 |
4 0.3690756302521009 0.6178571428571422 0.46789915966386564 0.3160714285714301 |
4 0.7055462184873948 0.6299107142857143 0.5815126050420169 0.7401785714285714 |
4 0.5016806722689076 0.5848214285714279 0.9966386554621849 0.8303571428571417 |
5 0.5404480743910461 0.5165178571428573 0.7360557932850555 0.34375000000000006 |
6 0.3825905539772727 0.4522727272727273 0.5961470170454546 0.42500000000000004 |
4 0.5118062627291242 0.12169042769857434 0.5103742362525457 0.21486761710794297 |
5 0.5513401693926787 0.4220795005674491 0.3446478251866939 0.40665859561409756 |
0 0.41563197544642855 0.45044642857142864 0.3000139508928571 0.6741071428571429 |
0 0.7531738281249999 0.292410714285714 0.49365234375000017 0.2401785714285712 |
3 0.17732482698961916 0.3165178571428568 0.17229671280276798 0.18839285714285692 |
3 0.15486591695501734 0.4941964285714286 0.1997837370242215 0.23482142857142863 |
3 0.33386678200692044 0.3285714285714286 0.1515138408304498 0.13214285714285715 |
3 0.5279519896193772 0.21651785714285712 0.19777249134948094 0.23482142857142854 |
3 0.7330990484429065 0.13794642857142855 0.2701773356401385 0.27053571428571427 |
3 0.5846020761245665 0.39776785714285706 0.20112456747404836 0.26696428571428577 |
3 0.3405709342560554 0.5968749999999998 0.21453287197231827 0.2776785714285714 |
3 0.3714100346020762 0.8205357142857145 0.21587370242214537 0.2874999999999999 |
3 0.6234861591695503 0.8866071428571428 0.2775519031141869 0.2267857142857143 |
3 0.8797902249134947 0.8299107142857138 0.24041955017301006 0.34017857142857055 |
3 0.8091911764705884 0.6089285714285714 0.22660034602076123 0.30357142857142855 |
0 0.4737374441964283 0.5343749999999999 0.20751953124999992 0.20982142857142835 |
0 0.8158482142857142 0.4370535714285715 0.2039620535714286 0.16875 |
End of preview.
Dataset Card for e-waste Data with YOLO labels
This dataset contains images and the connected YOLO labels. The labelling was done on LabelStudio and the files then exported.
The images were the product of an effort by GIZ Data Lab to make image data available that could be used for machine learning to tackle the e-waste challenge. The images originate from several scrapyards in Ghana collected in October 2025.
- Curated by: GIZ Data Lab
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