Datasets:

mesolitica
/

google-image-malaysia-location-dedup

Tasks:

Image Feature Extraction

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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 2 new columns ({'shards', 'version'}) and 4 missing columns ({'filename', 'no', 'selected_indices', 'keyword'}).

This happened while the json dataset builder was generating data using

hf://datasets/mesolitica/google-image-malaysia-location-dedup/embedding/index.json (at revision dc5881f9ff33f960877dc1d2e10ecce024f49eba)

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 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, 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 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              shards: list<item: struct<column_encodings: list<item: string>, column_names: list<item: string>, column_sizes: list<item: int64>, compression: null, format: string, hashes: list<item: string>, raw_data: struct<basename: string, bytes: int64, hashes: struct<sha1: string, xxh64: string>>, samples: int64, size_limit: int64, version: int64, zip_data: null>>
                child 0, item: struct<column_encodings: list<item: string>, column_names: list<item: string>, column_sizes: list<item: int64>, compression: null, format: string, hashes: list<item: string>, raw_data: struct<basename: string, bytes: int64, hashes: struct<sha1: string, xxh64: string>>, samples: int64, size_limit: int64, version: int64, zip_data: null>
                    child 0, column_encodings: list<item: string>
                        child 0, item: string
                    child 1, column_names: list<item: string>
                        child 0, item: string
                    child 2, column_sizes: list<item: int64>
                        child 0, item: int64
                    child 3, compression: null
                    child 4, format: string
                    child 5, hashes: list<item: string>
                        child 0, item: string
                    child 6, raw_data: struct<basename: string, bytes: int64, hashes: struct<sha1: string, xxh64: string>>
                        child 0, basename: string
                        child 1, bytes: int64
                        child 2, hashes: struct<sha1: string, xxh64: string>
                            child 0, sha1: string
                            child 1, xxh64: string
                    child 7, samples: int64
                    child 8, size_limit: int64
                    child 9, version: int64
                    child 10, zip_data: null
              version: int64
              to
              {'filename': Value(dtype='string', id=None), 'keyword': Value(dtype='string', id=None), 'no': Value(dtype='int64', id=None), 'selected_indices': Sequence(feature=Value(dtype='int64', id=None), length=-1, 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 1319, in compute_config_parquet_and_info_response
                  parquet_operations, partial = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 912, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, 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 2013, 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 2 new columns ({'shards', 'version'}) and 4 missing columns ({'filename', 'no', 'selected_indices', 'keyword'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/mesolitica/google-image-malaysia-location-dedup/embedding/index.json (at revision dc5881f9ff33f960877dc1d2e10ecce024f49eba)
              
              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.

filename string	keyword string	no int64	selected_indices sequence
train-00048-of-01000.parquet	Bekenu - Beg Berkunci Bekenu	15	[ 4019, 4023, 4025, 4028, 4032 ]
train-00242-of-01000.parquet	Jalan Senohong 17/44 Shah Alam	46	[ 2220, 2226 ]
train-00812-of-01000.parquet	Taman Megah Jaya Ayer Tawar	16	[ 2556, 2560, 2575, 2577, 2586, 2587 ]
train-00299-of-01000.parquet	Kampung Batu 13 Teluk Intan	17	[ 397, 404 ]
train-00272-of-01000.parquet	Jalan Ushawan Kuala Lumpur	16	[ 666, 684, 701, 708, 723, 732, 743, 752 ]
train-00911-of-01000.parquet	Taman Seri Sementa Klang	15	[ 1535, 1556, 1702, 1727, 1731, 1739, 1769, 1789 ]
train-00465-of-01000.parquet	Limbang Limbang	6	[ 720, 723, 727, 766, 769, 776, 785, 792, 804, 940, 959, 1025, 1037, 1079, 1107, 1120, 1152, 1166, 1205, 1208, 1249, 1256, 1261, 1262, 1263, 1285, 1286, 1325 ]
train-00284-of-01000.parquet	Kampung Aji Hilir Bota	75	[ 2312, 2314, 2316 ]
train-00356-of-01000.parquet	Kampung Palimbayan Kuala Lumpur	25	[ 579, 586, 622, 627, 634, 645 ]
train-00686-of-01000.parquet	Taman Bersatu (Jalan Betong) Pengkalan Hulu	32	[ 2975, 2979, 2982, 2986, 2987 ]
train-00199-of-01000.parquet	Jalan Imam Haji Ismail Machang	31	[]
train-00123-of-01000.parquet	Ijok Batang Berjuntai	9	[ 992, 1008, 1045, 1073, 1104, 1117 ]
train-00836-of-01000.parquet	Taman Orchidwood Kuching	5	[ 206, 254, 281, 283, 298, 306 ]
train-00427-of-01000.parquet	Kompleks Perumahan Guuru Langkawi	38	[ 3947, 3955 ]
train-00351-of-01000.parquet	Kampung Nerusa Johol	62	[ 3422, 3425, 3431 ]
train-00260-of-01000.parquet	Jalan TR 9/2 Petaling Jaya	35	[ 1232, 1238, 1258, 1274, 1278, 1282 ]
train-00359-of-01000.parquet	Kampung Pasir ( Kuala Dipang ) Jeram	74	[ 2711, 2713, 2718 ]
train-00466-of-01000.parquet	Lintang Damai Bukit Mertajam	53	[ 2265, 2283, 2287 ]
train-00377-of-01000.parquet	Kampung Semadong Kangar	22	[ 781, 782, 787, 795 ]
train-00387-of-01000.parquet	Kampung Sungai Kota Tanjong Piandang	45	[ 2525, 2526, 2529 ]
train-00372-of-01000.parquet	Kampung Rantau Panjang Kampung Gajah	3	[ 103, 104 ]
train-00627-of-01000.parquet	Senai Industrial Park Senai	6	[ 2089, 2119, 2137, 2168, 2210, 2217, 2227, 2255, 2284, 2354, 2363, 2374, 2429, 2545, 2613, 2641, 2693 ]
train-00791-of-01000.parquet	Taman Kuala Idaman Masjid Tanah	6	[ 2677, 2699, 2700 ]
train-00369-of-01000.parquet	Kampung Pulau Kudur Kota Bharu	73	[ 2659, 2661, 2675, 2676, 2687, 2690 ]
train-00549-of-01000.parquet	Parit Simin Semerah	1	[ 17, 26, 28, 32 ]
train-00350-of-01000.parquet	Kampung Mukut Mersing	60	[ 3306, 3328, 3401, 3405, 3434, 3454 ]
train-00483-of-01000.parquet	Lorong Masjid India (2 & 4) Kuala Lumpur	16	[ 382, 390, 406, 434, 455, 504, 506, 511, 533 ]
train-00050-of-01000.parquet	Beluran	26	[ 1836, 1839, 1851, 1872, 1901, 1913, 1923, 1924, 1935, 1936, 1982, 2010, 2036, 2040, 2104, 2147, 2234, 2245, 2247, 2255, 2256, 2264, 2286, 2298, 2303, 2314, 2320, 2321, 2327, 2375 ]
train-00332-of-01000.parquet	Kampung Kolam Pendang	27	[ 1574, 1577, 1589, 1600, 1614 ]
train-00748-of-01000.parquet	Taman Golf Heights Seremban	27	[ 3278, 3293, 3302 ]
train-00309-of-01000.parquet	Kampung Bukit Semanggol Simpang Ampat Semanggol	117	[ 3772, 3786, 3801, 3876, 3877, 3901, 3902 ]
train-00588-of-01000.parquet	Plaza Hang Tuah Melaka	16	[ 1637, 1638, 1663, 1673, 1678, 1691, 1700, 1714, 1724, 1728, 1739, 1749, 1760, 1774, 1779, 1782, 1783, 1805, 1810 ]
train-00267-of-01000.parquet	Jalan Teluk Gadong Kuala Lumpur	9	[ 743, 779, 780 ]
train-00318-of-01000.parquet	Kampung Gandai Ayer Hitam	53	[ 2866, 2875 ]
train-00236-of-01000.parquet	Jalan Salung 33/26 Shah Alam	6	[ 332, 336 ]
train-00204-of-01000.parquet	Jalan Jerantut 26/128 Shah Alam	67	[ 3340, 3348, 3355 ]
train-00282-of-01000.parquet	Kabong Kabong	0	[ 1, 20, 27, 29, 56, 61, 67, 95, 116, 123, 143, 172, 190, 218, 220, 234, 238, 251, 275, 303 ]
train-00484-of-01000.parquet	Lorong Muntahah (1 - 3C) Kota Kinabalu	31	[ 1105, 1115 ]
train-00100-of-01000.parquet	Empangan Timah Tassoh JPS Kangar	3	[ 57, 72, 102, 126 ]
train-00406-of-01000.parquet	Kampung Wak Musa Pekan Nenas	66	[ 2359 ]
train-00702-of-01000.parquet	Taman Bukit Mewah Semenyih	1	[ 464, 524, 525, 533, 546, 552, 612, 709, 776, 973 ]
train-00944-of-01000.parquet	Taman Tas Mahkota Kuantan	9	[ 2675, 2688, 2718, 2725, 2746, 2747, 2788, 2796, 2807, 2811, 2824, 2854 ]
train-00297-of-01000.parquet	Kampung Baru Senggarang	2	[ 36, 40, 45, 56, 62 ]
train-00657-of-01000.parquet	Sungai Punai Pontian	10	[ 821, 823, 837 ]
train-00379-of-01000.parquet	Kampung Seri Merlow Lubok China	82	[ 3039, 3042, 3054 ]
train-00167-of-01000.parquet	Jalan Betek Pelabuhan Klang	11	[ 504, 513, 517, 522, 528 ]
train-00331-of-01000.parquet	Kampung Klau Kechil Raub	49	[ 2242, 2243, 2246, 2255, 2256 ]
train-00394-of-01000.parquet	Kampung Tanjong Baru Kuala Berang	53	[ 1597 ]
train-00752-of-01000.parquet	Taman Harmoni Impian Sungai Pelek	9	[ 790, 792 ]
train-00383-of-01000.parquet	Kampung Stenggang Bau	109	[ 3349, 3354, 3384, 3415 ]
train-00303-of-01000.parquet	Kampung Bendang Sera Serdang	52	[ 1288, 1292 ]
train-00296-of-01000.parquet	Kampung Baru Seberang Kuala Lumpur	56	[ 3522, 3552, 3598, 3616, 3621 ]
train-00380-of-01000.parquet	Kampung Serting Tengah Batu Kikir	44	[ 1680, 1691, 1700, 1745 ]
train-00175-of-01000.parquet	Jalan Cantek Johor Bahru	16	[ 964, 984, 986, 991, 992, 998 ]
train-00355-of-01000.parquet	Kampung Padang Ulu Takir Kuala Terengganu	36	[ 1751, 1753 ]
train-00162-of-01000.parquet	Jalan Bedok Kuala Lumpur	77	[ 3136, 3141, 3149, 3152, 3158, 3164, 3166 ]
train-00531-of-01000.parquet	Paka	4	[ 235, 236, 241, 249, 257, 266, 297, 305, 307, 319, 327, 349, 390, 411, 425, 436, 438, 441, 442, 463, 491, 517, 521, 525, 529, 542, 568, 598, 602, 608, 630, 637, 654, 659, 663, 681, 719, 721, 737, 749, 768, 769, 784, 795, 799, 849, 851, 877, 888 ]
train-00367-of-01000.parquet	Kampung Petaseh Kuala Klawang	3	[ 56, 68, 72 ]
train-00210-of-01000.parquet	Jalan Kekwa Serendah	48	[ 3349, 3477, 3480, 3489, 3501 ]
train-00089-of-01000.parquet	Dataran Pahlawan Melaka	0	[ 0, 2, 12, 18, 34, 53, 57, 62, 71, 82, 85, 89, 92, 110, 116, 121, 127, 137, 147, 151, 159, 164, 207, 226, 250, 251, 279, 286, 290, 291, 297, 306, 314, 318, 319, 331 ]
train-00785-of-01000.parquet	Taman King Dom Kuching	45	[ 2555, 2559, 2560, 2568, 2570 ]
train-00588-of-01000.parquet	Pokok Asam Simpang Ampat	32	[ 3491, 3498, 3503 ]
train-00957-of-01000.parquet	Taman Villa Mutiara Jerantut	21	[ 2617, 2618, 2631, 2642, 2646, 2653 ]
train-00228-of-01000.parquet	Jalan Pulau Ketam (U10/61A - U10/62D) Shah Alam	8	[ 316, 319, 329 ]
train-00275-of-01000.parquet	Jalan wangsa Delima 1A Kuala Lumpur	45	[ 1787, 1808, 1828, 1838, 1846 ]
train-00079-of-01000.parquet	Changkat Kampar Kuala Kangsar	26	[ 3156, 3174 ]
train-00396-of-01000.parquet	Kampung Tebing Tinggi Segamat	43	[ 3215, 3219, 3222 ]
train-00573-of-01000.parquet	Persiaran Jelutong Kuala Lumpur	16	[ 2152, 2166, 2196, 2197, 2238, 2258 ]
train-00163-of-01000.parquet	Jalan Belalang Enam 20/7F Shah Alam	8	[ 180, 189, 190, 191 ]
train-00806-of-01000.parquet	Taman Manggis Bukit Mertajam	20	[ 4037 ]
train-00756-of-01000.parquet	Taman Idaman Kuantan	18	[ 1550, 1564, 1575, 1580, 1586, 1648 ]
train-00082-of-01000.parquet	Cherating Kuantan	1	[ 695, 740, 752, 825, 869, 895, 920, 941, 1004, 1009, 1051, 1085, 1185, 1192, 1194, 1216, 1224, 1228, 1286, 1291, 1348 ]
train-00576-of-01000.parquet	Persiaran Saujana Shah Alam	7	[ 675, 690, 728, 734, 752, 763, 779 ]
train-00318-of-01000.parquet	Kampung Gading Galuh Pulai Chondong	4	[ 93, 111 ]
train-00344-of-01000.parquet	Kampung Manikavasagam Pulau Carey	25	[ 723, 724, 726, 747, 750, 758, 762 ]
train-00993-of-01000.parquet	Vistaria Residensi Kuala Lumpur	15	[ 3363, 3366, 3385, 3398, 3478, 3481, 3556, 3614, 3736 ]
train-00808-of-01000.parquet	Taman Mas Ria Muar	35	[ 2868, 2874, 2876, 2888 ]
train-00959-of-01000.parquet	Taman Warisan Kuala Krai	4	[ 1220, 1228, 1277, 1286 ]
train-00157-of-01000.parquet	Jalan Balakong Jaya (23, 23A, 23B, 24) Seri Kembangan	3	[ 157 ]
train-00041-of-01000.parquet	Batu Balai Damak	71	[ 3930, 3970 ]
train-00740-of-01000.parquet	Taman Duranta Seremban	5	[ 157, 182, 199, 207, 219, 221 ]
train-00263-of-01000.parquet	Jalan Taming Sari Melaka	10	[ 1233, 1267, 1287, 1290, 1299, 1331, 1354, 1373, 1380, 1392, 1436, 1490, 1497, 1527, 1531, 1543, 1566, 1585, 1596, 1598, 1637, 1641, 1645, 1648 ]
train-00823-of-01000.parquet	Taman Molek Gemas	31	[ 3300, 3306, 3314, 3320 ]
train-00001-of-01000.parquet	Alor Malai Alor Setar	4	[ 341, 345, 346, 351, 364, 400, 410, 420, 430, 534, 540, 591, 613, 631, 653, 662, 694, 704, 712, 729, 733, 776, 788, 795, 813, 817, 824, 886, 936, 955 ]
train-00417-of-01000.parquet	Kementerian Kewangan Sabah Kota Kinabalu	10	[ 1406, 1418, 1437, 1467, 1476, 1505 ]
train-00168-of-01000.parquet	Jalan Bringin Kluang	15	[ 973, 976, 993 ]
train-00375-of-01000.parquet	Kampung Seberang Kodiang	29	[ 831, 833 ]
train-00154-of-01000.parquet	Jalan Ayer Molek Kuala Lumpur	56	[ 3249, 3253, 3259 ]
train-00299-of-01000.parquet	Kampung Batu 26 Tanjong Tualang	78	[ 1590, 1593 ]
train-00424-of-01000.parquet	Klang - Peti Surat 95 Klang	33	[ 3923, 3929, 3930 ]
train-00658-of-01000.parquet	Sungai Sumun - Peti Surat Sungai Sumun	43	[ 3963, 3965, 3967 ]
train-00473-of-01000.parquet	Lorong Bunga Kenanga Kecil Kota Kinabalu	42	[ 1884 ]
train-00773-of-01000.parquet	Taman Johor Jaya Johor Bahru	11	[ 2098, 2099, 2103, 2110, 2146, 2155, 2158, 2191, 2194, 2236, 2266, 2290, 2362, 2370, 2412, 2417, 2418, 2428, 2439, 2457, 2467, 2473, 2514, 2579, 2719 ]
train-00332-of-01000.parquet	Kampung Kolam Kedai Menanti Melor	20	[]
train-00572-of-01000.parquet	Persiaran Geleri Kepayang Ipoh	27	[ 2657 ]
train-00723-of-01000.parquet	Taman Danau Kota Kuala Lumpur	4	[ 614, 621, 656, 659, 663, 684, 693, 719, 733, 749, 791, 834, 846, 861, 920, 929, 932, 948, 951, 962, 973, 1021, 1045, 1082, 1088, 1100, 1101, 1107, 1217, 1235, 1244 ]
train-00342-of-01000.parquet	Kampung Lubok Gong Rantau Panjang	10	[ 302, 318, 326 ]
train-00343-of-01000.parquet	Kampung Mak Chik Alor Setar	24	[ 868, 870 ]
train-00380-of-01000.parquet	Kampung Seterus Sauk	62	[ 2225, 2228 ]
train-00892-of-01000.parquet	Taman Sentosa Gemas	18	[ 1205, 1252, 1302, 1320 ]

End of preview.

Google Image Malaysia Location Dedup

Original dataset https://huggingface.co./datasets/malaysia-ai/crawl-google-image-malaysia-location

Source code at https://github.com/mesolitica/malaysian-dataset/tree/master/vlm/dedup-malaysia-location

Dedup 50% similar

dedup-0.5.jsonl, total deduped 227937 images,

{'filename': 'train-00812-of-01000.parquet',
 'keyword': 'Taman Megah Jaya Ayer Tawar',
 'no': 16,
 'selected_indices': [2556, 2559, 2575, 2577, 2586, 2587, 2595]}

Dedup 60% similar

dedup-0.6.jsonl, total deduped 487301 images,

{'filename': 'train-00404-of-01000.parquet',
 'keyword': 'Kampung Tok Wan Nik Padang Besar',
 'no': 92,
 'selected_indices': [2100, 2102, 2103, 2104]}

filename is the parquet file from the original repository.
selected_indices is the index of dataframe of that filename.

Embedding

We convert to embedding using https://huggingface.co./google/siglip-base-patch16-512, we use MosaicML for faster indexing,

from streaming import MDSWriter
from streaming.base.format.mds.encodings import Encoding, _encodings
from streaming import LocalDataset
import streaming
import numpy as np
from tqdm import tqdm

class Float32(Encoding):
    def encode(self, obj) -> bytes:
        return obj.tobytes()

    def decode(self, data: bytes):
        return np.frombuffer(data, np.float32)

_encodings['float32'] = Float32

dataset = LocalDataset('embedding')

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