Datasets:
image imagewidth (px) 566 1.13k | image_id stringlengths 13 13 | split stringclasses 1
value | width int32 566 1.13k | height int32 640 1.25k | annotations dict |
|---|---|---|---|---|---|
kh_doc_000000 | train | 800 | 1,130 | {
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kh_doc_000001 | train | 720 | 960 | {
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kh_doc_000002 | train | 600 | 850 | {
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kh_doc_000003 | train | 816 | 1,056 | {
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kh_doc_000004 | train | 800 | 1,130 | {
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kh_doc_000005 | train | 640 | 1,000 | {
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kh_doc_000006 | train | 600 | 850 | {
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kh_doc_000007 | train | 566 | 800 | {
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kh_doc_000008 | train | 950 | 1,250 | {
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kh_doc_000009 | train | 800 | 1,130 | {
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kh_doc_000010 | train | 566 | 800 | {
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kh_doc_000011 | train | 600 | 850 | {
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kh_doc_000012 | train | 900 | 640 | {
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kh_doc_000013 | train | 566 | 800 | {
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kh_doc_000014 | train | 1,130 | 800 | {
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kh_doc_000015 | train | 640 | 1,000 | {
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kh_doc_000016 | train | 816 | 1,056 | {
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kh_doc_000017 | train | 760 | 1,075 | {
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kh_doc_000018 | train | 950 | 1,250 | {
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kh_doc_000019 | train | 950 | 1,250 | {
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kh_doc_000020 | train | 640 | 1,000 | {
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kh_doc_000021 | train | 900 | 640 | {
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kh_doc_000022 | train | 600 | 850 | {
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kh_doc_000023 | train | 1,056 | 816 | {
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kh_doc_000024 | train | 640 | 1,000 | {
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kh_doc_000025 | train | 816 | 1,056 | {
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kh_doc_000026 | train | 816 | 1,056 | {
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kh_doc_000027 | train | 800 | 1,130 | {
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kh_doc_000028 | train | 950 | 1,250 | {
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kh_doc_000029 | train | 900 | 640 | {
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kh_doc_000030 | train | 1,130 | 800 | {
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kh_doc_000031 | train | 950 | 1,250 | {
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kh_doc_000032 | train | 1,056 | 816 | {
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kh_doc_000033 | train | 760 | 1,075 | {
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kh_doc_000034 | train | 720 | 960 | {
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kh_doc_000035 | train | 760 | 1,075 | {
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kh_doc_000036 | train | 950 | 1,250 | {
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kh_doc_000037 | train | 600 | 850 | {
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kh_doc_000038 | train | 720 | 960 | {
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kh_doc_000039 | train | 850 | 1,180 | {
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kh_doc_000040 | train | 1,130 | 800 | {
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kh_doc_000041 | train | 566 | 800 | {
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kh_doc_000042 | train | 566 | 800 | {
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kh_doc_000043 | train | 900 | 640 | {
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kh_doc_000044 | train | 1,056 | 816 | {
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kh_doc_000045 | train | 1,056 | 816 | {
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kh_doc_000046 | train | 1,130 | 800 | {
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kh_doc_000047 | train | 566 | 800 | {
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kh_doc_000048 | train | 720 | 960 | {
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kh_doc_000049 | train | 760 | 1,075 | {
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0.18790699541568756,
0.27... | |
kh_doc_000050 | train | 800 | 1,130 | {
"bbox": [
[
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],
[
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],
[
0.1237500011920929,
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0.... | |
kh_doc_000051 | train | 850 | 1,180 | {
"bbox": [
[
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],
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],
[
0.25999999046325684,
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0... | |
kh_doc_000052 | train | 950 | 1,250 | {
"bbox": [
[
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],
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[
0.19526299834251404,
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... | |
kh_doc_000053 | train | 900 | 640 | {
"bbox": [
[
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],
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],
[
0.7016670107841492,
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0.... | |
kh_doc_000054 | train | 600 | 850 | {
"bbox": [
[
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],
[
0.47999998927116394,
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0.7... | |
kh_doc_000055 | train | 800 | 1,130 | {
"bbox": [
[
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],
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],
[
0.1393750011920929,
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0... | |
kh_doc_000056 | train | 1,056 | 816 | {
"bbox": [
[
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],
[
0.49195098876953125,
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0.853... | |
kh_doc_000057 | train | 800 | 1,130 | {
"bbox": [
[
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],
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[
0.42625001072883606,
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0.68... | |
kh_doc_000058 | train | 1,056 | 816 | {
"bbox": [
[
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0.4805870056152344,
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0.821022987365... | |
kh_doc_000059 | train | 640 | 1,000 | {
"bbox": [
[
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],
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[
0.502344012260437,
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0.854... | |
kh_doc_000060 | train | 640 | 1,000 | {
"bbox": [
[
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],
[
0.14765599370002747,
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... | |
kh_doc_000061 | train | 900 | 640 | {
"bbox": [
[
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],
[
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0.15222199261188507,
0.04843800142407417
],
[
0.6994439959526062,
0.1445309966802597,
0.26777800917625... | |
kh_doc_000062 | train | 900 | 640 | {
"bbox": [
[
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0.10999999940395355,
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],
[
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0.1166670024394989,
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],
[
0.7044439911842346,
0.13515600562095642,
0... | |
kh_doc_000063 | train | 800 | 1,130 | {
"bbox": [
[
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],
[
0.2056249976158142,
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0.26875001192092896,
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],
[
0.49937498569488525,
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0.... | |
kh_doc_000064 | train | 640 | 1,000 | {
"bbox": [
[
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],
[
0.778905987739563,
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0.26406198740005493,
0.02199999988079071
],
[
0.3125,
0.15649999678134918,
0.428124994039535... | |
kh_doc_000065 | train | 1,056 | 816 | {
"bbox": [
[
0.8451700210571289,
0.08517199754714966,
0.16572000086307526,
0.030636999756097794
],
[
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0.08901499956846237,
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],
[
0.8811550140380859,
0.17095600068569183,
0... | |
kh_doc_000066 | train | 900 | 640 | {
"bbox": [
[
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],
[
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0.2044440060853958,
0.03281300142407417
],
[
0.6944440007209778,
0.1578119993209839,
0.... | |
kh_doc_000067 | train | 800 | 1,130 | {
"bbox": [
[
0.5249999761581421,
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0.8324999809265137,
0.023009000346064568
],
[
0.4975000023841858,
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0.8525000214576721,
0.02389400079846382
],
[
0.4506250023841858,
0.15929199755191803,
0.75... | |
kh_doc_000068 | train | 720 | 960 | {
"bbox": [
[
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0.2416670024394989,
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],
[
0.2152779996395111,
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0.21944400668144226,
0.01875000074505806
],
[
0.2569440007209778,
0.14479200541973114,
0.... | |
kh_doc_000069 | train | 850 | 1,180 | {
"bbox": [
[
0.7411760091781616,
0.07796599715948105,
0.36470600962638855,
0.03220300003886223
],
[
0.8235290050506592,
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0.20000000298023224,
0.03305099904537201
],
[
0.8205879926681519,
0.18262700736522675,
0.2... | |
kh_doc_000070 | train | 900 | 640 | {
"bbox": [
[
0.1261110007762909,
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0.13666699826717377,
0.04374999925494194
],
[
0.13666699826717377,
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0.15777799487113953,
0.04062499850988388
],
[
0.25555598735809326,
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0.... | |
kh_doc_000071 | train | 720 | 960 | {
"bbox": [
[
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0.26527801156044006,
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],
[
0.2104170024394989,
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0.2569440007209778,
0.02187499962747097
],
[
0.7159720063209534,
0.06041700020432472,
0.... | |
kh_doc_000072 | train | 1,056 | 816 | {
"bbox": [
[
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0.1543560028076172,
0.0441179983317852
],
[
0.7215909957885742,
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0.23674200475215912,
0.04166699945926666
],
[
0.5132579803466797,
0.24264700710773468,
0.142... | |
kh_doc_000073 | train | 950 | 1,250 | {
"bbox": [
[
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0.0812000036239624,
0.13368399441242218,
0.019999999552965164
],
[
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0.20105299353599548,
0.030400000512599945
],
[
0.24578900635242462,
0.14720000326633453,
... | |
kh_doc_000074 | train | 800 | 1,130 | {
"bbox": [
[
0.13625000417232513,
0.07876099646091461,
0.1525000035762787,
0.023009000346064568
],
[
0.1706250011920929,
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0.2212499976158142,
0.023009000346064568
],
[
0.7193750143051147,
0.0783189982175827,
0.2... | |
kh_doc_000075 | train | 950 | 1,250 | {
"bbox": [
[
0.1484210044145584,
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0.12631599605083466,
0.019200000911951065
],
[
0.1599999964237213,
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0.14947399497032166,
0.019200000911951065
],
[
0.15684199333190918,
0.12600000202655792,
... | |
kh_doc_000076 | train | 1,130 | 800 | {
"bbox": [
[
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0.16106200218200684,
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],
[
0.1703539937734604,
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0.22389400005340576,
0.04125000163912773
],
[
0.13805299997329712,
0.30000001192092896,
... | |
kh_doc_000077 | train | 760 | 1,075 | {
"bbox": [
[
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0.22105300426483154,
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],
[
0.8006579875946045,
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0.21710500121116638,
0.028836999088525772
],
[
0.22565799951553345,
0.18279099464416504,
... | |
kh_doc_000078 | train | 1,130 | 800 | {
"bbox": [
[
0.14203499257564545,
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0.153097003698349,
0.042500000447034836
],
[
0.7044249773025513,
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0.23539799451828003,
0.04125000163912773
],
[
0.48849600553512573,
0.19750000536441803,
0.... | |
kh_doc_000079 | train | 640 | 1,000 | {
"bbox": [
[
0.20859399437904358,
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0.27656200528144836,
0.02500000037252903
],
[
0.19687500596046448,
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0.25312501192092896,
0.017999999225139618
],
[
0.721094012260437,
0.05999999865889549,
... | |
kh_doc_000080 | train | 1,056 | 816 | {
"bbox": [
[
0.13541699945926666,
0.12806400656700134,
0.14583300054073334,
0.04289200156927109
],
[
0.6998109817504883,
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0.18181799352169037,
0.03799000009894371
],
[
0.4829550087451935,
0.22855399549007416,
0.1... | |
kh_doc_000081 | train | 800 | 1,130 | {
"bbox": [
[
0.17812499403953552,
0.0752210021018982,
0.1537500023841858,
0.02123899944126606
],
[
0.20499999821186066,
0.10044199973344803,
0.20749999582767487,
0.022123999893665314
],
[
0.3812499940395355,
0.1526550054550171,
0.5... | |
kh_doc_000082 | train | 720 | 960 | {
"bbox": [
[
0.16319400072097778,
0.057812999933958054,
0.11249999701976776,
0.026041999459266663
],
[
0.20347200334072113,
0.08385399729013443,
0.19305600225925446,
0.019791999831795692
],
[
0.29236099123954773,
0.1421869993209839,
... | |
kh_doc_000083 | train | 640 | 1,000 | {
"bbox": [
[
0.11796899884939194,
0.05700000002980232,
0.12343800067901611,
0.019999999552965164
],
[
0.16171899437904358,
0.08349999785423279,
0.2109380066394806,
0.017000000923871994
],
[
0.22499999403953552,
0.11749999970197678,
... | |
kh_doc_000084 | train | 720 | 960 | {
"bbox": [
[
0.13402800261974335,
0.06458300352096558,
0.16249999403953552,
0.02500000037252903
],
[
0.48888900876045227,
0.10781300067901611,
0.8722220063209534,
0.02812499925494194
],
[
0.3763889968395233,
0.1479170024394989,
0.6... | |
kh_doc_000085 | train | 800 | 1,130 | {
"bbox": [
[
0.11749999970197678,
0.05177000164985657,
0.11999999731779099,
0.022123999893665314
],
[
0.14000000059604645,
0.0769909992814064,
0.16500000655651093,
0.023009000346064568
],
[
0.2512499988079071,
0.1163719967007637,
0... | |
kh_doc_000086 | train | 640 | 1,000 | {
"bbox": [
[
0.18984399735927582,
0.07649999856948853,
0.1640619933605194,
0.028999999165534973
],
[
0.1796880066394806,
0.11500000208616257,
0.14374999701976776,
0.029999999329447746
],
[
0.7124999761581421,
0.07599999755620956,
0... |
End of preview. Expand in Data Studio
Synthetic Khmer Document Text-Line Detection Dataset
Synthetic dataset for single-class text-line detection on Cambodian (Khmer) official documents — press releases, ministry letters, formal memos.
Generated with a procedural Pillow-based pipeline featuring:
- 8 layout templates (standard, letter, announcement, report, sparse, two-column, memo, plain)
- 12 page sizes from A5 to A4-landscape
- Variable margins, font sizes, line spacing, and indentation
- Photometric augmentations (brightness, blur, noise, JPEG, shadow, vignette, fold/crease)
Class
| ID | Name | Description |
|---|---|---|
| 0 | text_line |
Any horizontal line of text |
Dataset statistics
| Split | Images |
|---|---|
| train | 1955 |
| val | 345 |
Schema
{
"image": Image(),
"image_id": Value("string"), # e.g. "kh_doc_000042"
"split": Value("string"), # "train" | "val"
"width": Value("int32"),
"height": Value("int32"),
"annotations": Sequence({
"bbox": Sequence(Value("float32"), length=4), # [cx,cy,w,h] normalised
"cls_id": Value("int32"), # always 0
}),
}
Load with HF Datasets
from datasets import load_dataset
ds = load_dataset("Darayut/khmer-textline-dataset")
sample = ds["train"][0]
print(sample["image"]) # PIL Image
print(sample["annotations"]) # dict of lists
Raw YOLO files
data/yolo_raw.zip contains the native YOLO directory layout
(images/, labels/, dataset.yaml) for direct Ultralytics training:
from huggingface_hub import hf_hub_download
import zipfile, pathlib
zip_path = hf_hub_download(
repo_id = "Darayut/khmer-textline-dataset",
filename = "data/yolo_raw.zip",
repo_type = "dataset",
)
with zipfile.ZipFile(zip_path) as zf:
zf.extractall("./khmer_doc_yolo")
# yolo train data=khmer_doc_yolo/dataset.yaml model=yolo11n.pt
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