Training - UB-Mannheim/kraken GitHub Wiki

Training

Examples of trainings for kraken

This sequence of examples was focused on getting a good kraken model for historic prints, especially for Fraktur script, but also with support for historic and modern Antiqua fonts.

frak2021

The training used the same data sets for training and evaluation as a previous training which was done with Tesseract.

It was done the latest kraken code plus additional fixes for a division by null and multithreading.

nohup time ketos train -t list.train -e list.eval -o frak2021_gpu -d cuda:0 | tee -a frak2021_gpu.log
nohup: Eingabe wird ignoriert und leite Standardfehlerausgabe auf Standardausgabe umgeleitet
Building training set
Building validation set
[52.2009] alphabet mismatch: chars in training set only: {'~', 'แฝ', 'ล‘', 'ร€', 'ฯ‡', 'โ‚†', 'ร’', 'แฟณ', 'รฏ', 'แธก', 'ฯŽ', 'แผ„', 'ฮผ', 'ฯ‹', 'โ˜›', 'แผธ', 'ฯ†', 'ล', 'แผฐ', 'ฮพ', 'อ—', 'โ„–', 'โ…š', 'ฤ‘', 'ฯ', 'ฯ–', 'แฝ€', 'ฮฆ', 'ฮ”', '_', 'ฯฐ', 'ั’', 'แผ€', 'ฮค', 'แด', 'ฮบ', 'ร“', 'ฮ‘', 'รˆ', 'โ„ฒ', 'แผ', 'ร‰', 'ร”', 'แฝฐ', '+', 'ฯŸ', 'โ€ฃ', 'ลฝ', 'ฮท', 'โ–ฒ', 'ฮ’', 'ฮฏ', 'โ…“', 'ลพ', 'ล ', 'โ“', 'แฝ', 'ฯŒ', 'ยฑ', 'ฮฌ', 'ฯˆ', 'โ‚ˆ', 'รฎ', 'โ™‚', 'โˆ™', 'ฮธ', 'โ…Ž', 'โ„ณ', 'ร‡', 'ั”', 'แฟ†', 'ฯ‘', 'โ‚„', 'ฯฑ', 'โœค', 'แฝธ', 'โ™€', 'โ…”', 'แผˆ', 'ฮด', 'แฝด', 'แผ‘', 'ษ”', 'ฮถ', 'แพฝ', 'แฝถ', 'รฌ', 'สž', 'โ…™', 'โ˜š', 'โ€›', 'โ„”', 'ฮฎ'} (not included in accuracy test during training) 
[52.2010] alphabet mismatch: chars in validation set only: {'ฯŠ'} (not trained) 
Initializing model โœ“
stage 1/โˆž
Accuracy report (1) 0.9612 1625450 63133
stage 2/โˆž
Accuracy report (2) 0.9604 1625450 64311
stage 3/โˆž
Accuracy report (3) 0.9578 1625450 68579
stage 4/โˆž
Accuracy report (4) 0.9519 1625450 78227
stage 5/โˆž
Accuracy report (5) 0.9428 1625450 92982
stage 6/โˆž
Accuracy report (6) 0.9421 1625450 94036
Moving best model frak2021_gpu_1.mlmodel (0.9611597061157227) to frak2021_gpu_best.mlmodel
154547.59user 1537.72system 29:15:33elapsed 148%CPU (0avgtext+0avgdata 5916304maxresident)k
385328inputs+453328outputs (68102177major+26511973minor)pagefaults 0swaps

Juristische Konsilien Tรผbingen

Get the data

The information here is no longer valid.

mkdir Juristische_Konsilien_Tuebingen
cd Juristische_Konsilien_Tuebingen
mkdir Training
cd Training
# Get image files.
wget --content-disposition https://files.transkribus.eu/Get?id=KWPGLJROIYFMCHXUKHROTBZI https://files.transkribus.eu/Get?id=FFZEEILUCMKWBMTOJNKXOQVO https://files.transkribus.eu/Get?id=DHTNZFGEQZVNFYSNNXSUGRZW https://files.transkribus.eu/Get?id=GLUHQPHRJZGIORAKNKSSHJHN https://files.transkribus.eu/Get?id=AXGGUKSGAASTMJLXHKLHTCMI https://files.transkribus.eu/Get?id=ZWNHITSTVYAYVRMXSACMYZSS https://files.transkribus.eu/Get?id=SJOIKRULJGZRUGOHANSBQCES https://files.transkribus.eu/Get?id=TXUOIQXJUFRBXPZXRIQKRGID https://files.transkribus.eu/Get?id=FBFDPQSANWKZHMEFPCAEBYGC https://files.transkribus.eu/Get?id=JJYAAMDIJMVXEMDQNQLFSNHP https://files.transkribus.eu/Get?id=CFHAJXQXKFTYWEMOXGMHIFTK https://files.transkribus.eu/Get?id=YJNIOPLPTVNXGLVKAYDWSZTJ https://files.transkribus.eu/Get?id=KSSNVVQMKCIPCOJNVRQIOIIH https://files.transkribus.eu/Get?id=OGZBGTHHLDZQRUFVQBDMDDPA https://files.transkribus.eu/Get?id=KYPDSMEVHCRVHUXKYNOXDTPT https://files.transkribus.eu/Get?id=UJUHLJMVEUIXOOVDPIQKPXFL https://files.transkribus.eu/Get?id=ZTTKWCFYHHJXPCJHVLGCFQCM https://files.transkribus.eu/Get?id=FOVUVMNPDJDZLKWIDYGJPLUX https://files.transkribus.eu/Get?id=DXRPIBLIMBBMIXVDXCSMOLDM https://files.transkribus.eu/Get?id=LZPWBKIXSJJIFAWPGUZSPVXT https://files.transkribus.eu/Get?id=HOHGKGGHOSQUUTAVACVQYWAL https://files.transkribus.eu/Get?id=GPQZHFWYWLZEXJCDTJKAALEE https://files.transkribus.eu/Get?id=TAAKRFOWGFBKCEFSHNQIVOIK https://files.transkribus.eu/Get?id=RSAIQAPCKZZTNAKLEHTWBPGM https://files.transkribus.eu/Get?id=NZGMAVAGNRHFMZNNUIJDTRLU https://files.transkribus.eu/Get?id=PCNIVHAJHFRBOYBPQERJTOED https://files.transkribus.eu/Get?id=OJKITNXIXTYUQPCZKYHQYFSD https://files.transkribus.eu/Get?id=SZJTDCPESLWRIHCZXMBEENTJ https://files.transkribus.eu/Get?id=LTREBTDWWJRERTPPUBONYEEH https://files.transkribus.eu/Get?id=KLINCPGDKEXBUQNBUDGJUOPY https://files.transkribus.eu/Get?id=ZGQKNMDFPAZNRBMMKSOZZHKA 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https://files.transkribus.eu/Get?id=MBLXSOIAFBZPRGCBEAWLSXIQ https://files.transkribus.eu/Get?id=JPTFEKQFLOIJABAHZPJGWTIX https://files.transkribus.eu/Get?id=GVOZSINXFOQINGIHOFLHZEXW https://files.transkribus.eu/Get?id=UIUAZQUCPLZMULJHNUGEYUKR https://files.transkribus.eu/Get?id=AJJVYMFQTWWIWJFCUKMJKJQP https://files.transkribus.eu/Get?id=GIJZOLKNHKZXBTBWPJJJLYYR https://files.transkribus.eu/Get?id=KRXUUEYRYVHLMMUFLKCGUQQH https://files.transkribus.eu/Get?id=RKUEWSQAKAWVGCJMBNOSIVNR https://files.transkribus.eu/Get?id=MNOPSPMISKMPTXUIJUSOEOCW https://files.transkribus.eu/Get?id=BFYUPHCCJDCUAZWITORDMLGX https://files.transkribus.eu/Get?id=APIACVAWACKQMPYJILFINMZT https://files.transkribus.eu/Get?id=TVBDRWEFCSENIIYFTLRYPEAA https://files.transkribus.eu/Get?id=GJYRHFRDWSHRLJVIGNKWWBZN https://files.transkribus.eu/Get?id=BQDKZIVQUJEXYYBCNVTBLHZM https://files.transkribus.eu/Get?id=DOIIRJFDXVHNAXFMUVFBPPYE https://files.transkribus.eu/Get?id=CSELZDAMABXKPWZNCSOHCBAF https://files.transkribus.eu/Get?id=SGFNBPQGXZMZAKGUSWXWOPVR https://files.transkribus.eu/Get?id=XQQIAQLQRMKMYCVHCLWHUYFQ https://files.transkribus.eu/Get?id=ROMIWFNFIBTGGCFLIZJJFWKU https://files.transkribus.eu/Get?id=KTUVRXMZBRHUYFVDAULYHKBC https://files.transkribus.eu/Get?id=AQHXCBAWQVKHUVUHZGDZPZUT https://files.transkribus.eu/Get?id=RXKIXYMKMLQBTJGXFYGCSGDY https://files.transkribus.eu/Get?id=UBEIKSGVTBMHIJPQOEGZSGWD https://files.transkribus.eu/Get?id=RWMVMWDCPNFBTQFGWQOAAVQQ https://files.transkribus.eu/Get?id=NYDHDAZGYHUDGXENOAASLFYF https://files.transkribus.eu/Get?id=SMVJNOMKZTUVAXKVDAJXSAZS https://files.transkribus.eu/Get?id=VIHRDXWGGOATSMCPZJJXAAXJ https://files.transkribus.eu/Get?id=YWFJNUVWHSZGMBDEZTJMCGFM https://files.transkribus.eu/Get?id=OXHBEFWYPNGMTTARWUPYSYUU https://files.transkribus.eu/Get?id=NDCENLOGOVSMHFPOTPOUCQKG https://files.transkribus.eu/Get?id=TIGUOIFRBCUMNOFZYARPGGRS https://files.transkribus.eu/Get?id=NKDMDKVMRTZWTKTRMCYDHFLW https://files.transkribus.eu/Get?id=IDIHIPZTDOXTOQCAWCBQMNOQ https://files.transkribus.eu/Get?id=IQOTZUSGWOKZEPBJBSYYJGTM https://files.transkribus.eu/Get?id=FRWNLSWXMYIPGQJGVPLWPIXT https://files.transkribus.eu/Get?id=QNXDOMQULVGUWUGHIKQDPCOB https://files.transkribus.eu/Get?id=EYISIDNNZSWKOQOYMQUTXVSK https://files.transkribus.eu/Get?id=RGFHPRMBPRYNEFLVEJTCOILG https://files.transkribus.eu/Get?id=FBZILEXIIRGVPPJLOYYSGHCD https://files.transkribus.eu/Get?id=KOIUIIXSXVJDFERSFIXMSZUL https://files.transkribus.eu/Get?id=OYLYXYLBLJYUJYOPLEFDHKLH https://files.transkribus.eu/Get?id=KJVSKBNNSJOQFGSRXVMRZYQR https://files.transkribus.eu/Get?id=UHAHGFFAMRNSTNQGMOBNQTUR https://files.transkribus.eu/Get?id=ADDQMOOXTWMOXGEEYMANWNJZ https://files.transkribus.eu/Get?id=HESNYZGFTYGUDFGZANLQLMOF https://files.transkribus.eu/Get?id=NQCXKFIDTGDACYHFUJZKJQDS https://files.transkribus.eu/Get?id=JZUMSCTEYEHMAOXDAXZNXGUX https://files.transkribus.eu/Get?id=IBERFVXKQCGEAHQNKPNCSNMA https://files.transkribus.eu/Get?id=FGLWDUKYAMWQIHEHKKQOOLMW https://files.transkribus.eu/Get?id=BCUVIEVSNFOUAYXSYCHNPXVP https://files.transkribus.eu/Get?id=XGPDURDECYWHYWZLJHDMIGLU https://files.transkribus.eu/Get?id=YSSYTELQQLMAHAVXQOGPUTUG https://files.transkribus.eu/Get?id=JOYKOTRPSTUFZXBORFKRCZJC https://files.transkribus.eu/Get?id=USOQSWUKNUEATWIZHMUYLDAF https://files.transkribus.eu/Get?id=VKIXEWUCBSZSKBBGIBOVUSUG https://files.transkribus.eu/Get?id=MEYLSJNTPSHUCXXUAJKCZPKA https://files.transkribus.eu/Get?id=XJDDZZAHHOSJGEPLXOBBRDOA https://files.transkribus.eu/Get?id=MYHOGTLJMXZUVRLJOSCUWWFC https://files.transkribus.eu/Get?id=BLASBUUDLXUNZMUIEVJUEJKJ https://files.transkribus.eu/Get?id=WGPNKBRBCKXVXKFONPETTISL https://files.transkribus.eu/Get?id=QMZIHCDKWOSFAPQVPENYRPHJ https://files.transkribus.eu/Get?id=KONWXWVKBGRGADHIGCFGFOXO https://files.transkribus.eu/Get?id=XXQAPZNSVNXIPGRGVHPTABFI https://files.transkribus.eu/Get?id=IPQPOXCWANMWHGBRMRKIYZLS https://files.transkribus.eu/Get?id=VIMEFFPNDFRWXEJQNXODRBEO https://files.transkribus.eu/Get?id=ZAABPSRTGFETETNGYMGLZCOL https://files.transkribus.eu/Get?id=VLSHOPUQJNHGWWSZKKXQAAHK https://files.transkribus.eu/Get?id=HZIGXWAUPNIABHHIBQHBTGEC https://files.transkribus.eu/Get?id=WDHNPKASTGLNHDIVJKSRVFMX https://files.transkribus.eu/Get?id=ZQWHSMWYZFDJUUDMGEYJYDDY https://files.transkribus.eu/Get?id=ZECCNUCTLIHMSLJPJVGAWUXP https://files.transkribus.eu/Get?id=XKKHGWQTAHWAULBAZBLBQCBQ https://files.transkribus.eu/Get?id=BDFTIFSQHNPGIQHLUSQBNZGL https://files.transkribus.eu/Get?id=NMFPJUPMQPKXBBLCBLNVDUQA https://files.transkribus.eu/Get?id=ZANEBGCYUOUQLYXHKOVSPMER https://files.transkribus.eu/Get?id=EGEUWBWWBYWLKJISSMKERPRZ https://files.transkribus.eu/Get?id=YFACZWEGHUMIJOSGFDIFIIXI https://files.transkribus.eu/Get?id=WXWUUGZKGYIKYFJVUPXLXNLN https://files.transkribus.eu/Get?id=BHERQQEMBDPKHNQCTTIABRAN https://files.transkribus.eu/Get?id=DKFQEVFTRHFUYTNWUWKGMDCC https://files.transkribus.eu/Get?id=MHUFOPWVDKPNENGOTPHMRBPG https://files.transkribus.eu/Get?id=PMKYLRJEIQBIIFQVGAAKBAYE https://files.transkribus.eu/Get?id=BTHWVSEFZFEQVWQXNLLVMQQX https://files.transkribus.eu/Get?id=YWYAOOYGHXSNXEMOWXFVFBBV https://files.transkribus.eu/Get?id=ZARBXUTLMISJJORKXYCCLKLJ https://files.transkribus.eu/Get?id=VIIJFLMYIRGLEPGPPAEUOHEG https://files.transkribus.eu/Get?id=BJAVSUFRATHIZMZJBFFVZLWY https://files.transkribus.eu/Get?id=XAFFBZVXTIOMURIZJANEDCRL https://files.transkribus.eu/Get?id=EADIASJDESBUIGBHBBRVBENC https://files.transkribus.eu/Get?id=UHXRNRWZURNNIITNZLBHIKAT https://files.transkribus.eu/Get?id=LYMSCWGIRTABXAXYSWMKLXFF https://files.transkribus.eu/Get?id=RVHBELCYHNQXRVOVSFHFPHUF https://files.transkribus.eu/Get?id=WYPAPFXUXDIYSHGCVUHPCDOE https://files.transkribus.eu/Get?id=RAAORHEZEHXFRPDCFVOUDSNE https://files.transkribus.eu/Get?id=AUMCSARBRKNJLWQGOEFAVEVN https://files.transkribus.eu/Get?id=LHPLORVBMFYIQXIFUOOQNWLF https://files.transkribus.eu/Get?id=YECVIISDTADFSAVYVVRULJED https://files.transkribus.eu/Get?id=BFQTOOGEOSYCFSTIJFYASZKN https://files.transkribus.eu/Get?id=PJXUZAFEEIUWRLDOENGGGYQM https://files.transkribus.eu/Get?id=YOHJAMQIAVSFHWRUESLIHEJH https://files.transkribus.eu/Get?id=EJXPQXSUQUIUSHKMECISPLRE https://files.transkribus.eu/Get?id=ETRCBMFJJFFSCBWGCWMKVYPU https://files.transkribus.eu/Get?id=RSYKJGUJTWKLWHUVWCZGVHBK https://files.transkribus.eu/Get?id=LHMYRDDJSVVHXCFZAPPIUYWS https://files.transkribus.eu/Get?id=JUQMUFIOQAYNHJSATWZVAVJK https://files.transkribus.eu/Get?id=WJLWJKXFVIOXLXEELWFAXEWS https://files.transkribus.eu/Get?id=XVFBFYOABAYUUBGEETZFNDUU https://files.transkribus.eu/Get?id=GSSITABWCVXAEYCPEGGAGWHO https://files.transkribus.eu/Get?id=HHQPEJLPUUSTOJVMGYHEMCWO https://files.transkribus.eu/Get?id=ETKNGULSXICMTXMSUNGUHAGR https://files.transkribus.eu/Get?id=EVVBDQATMBBOWZQVWWBGWFTE https://files.transkribus.eu/Get?id=JQNZVEEWHOTGPSGQWBJTYDXZ https://files.transkribus.eu/Get?id=HDJMNCKXBJTYRBANCDDNBOTV https://files.transkribus.eu/Get?id=FXWRRMVBGADVUBCLXQTUYPAT https://files.transkribus.eu/Get?id=YHIBTJOSJFQPMEFCMUASZYRR https://files.transkribus.eu/Get?id=WUALYWRDKBXDJKNGZGXEYHKQ https://files.transkribus.eu/Get?id=IENQXLHPZTOZJEENEIKRAKNU https://files.transkribus.eu/Get?id=ABWEDGWBHNRUHCDAZQBCHDHR https://files.transkribus.eu/Get?id=HOHZGRKUZEEHTQFOZIUVWRKG https://files.transkribus.eu/Get?id=TYEHTOEKTZMMCEFACTOKAUFI https://files.transkribus.eu/Get?id=DWSKPMUEJAIOJJYYGIRBIGCQ https://files.transkribus.eu/Get?id=JOUYTXSVTWDUMSRJHPBXFGKX https://files.transkribus.eu/Get?id=FGDLEJDZPQNYAMEAQXIQLDNK https://files.transkribus.eu/Get?id=JUXAJMEYQARQLPURIWTWGBBP https://files.transkribus.eu/Get?id=KRNJXKZJCTBMJJDLHHMMSINE https://files.transkribus.eu/Get?id=YULOHZIMFHEOJKWDHCFOLBUL https://files.transkribus.eu/Get?id=OFTLVWCMKVUPUZZJCRLXEIYS https://files.transkribus.eu/Get?id=YHRJAICVWUIQGVCSIPBMKEKH https://files.transkribus.eu/Get?id=JIYRQKQXDYYQJOFINPLTOQSZ https://files.transkribus.eu/Get?id=SSASFJCYPAANTCROPGETZUPP https://files.transkribus.eu/Get?id=QEXKWYLPSRKUWBCIGQNHHPTP https://files.transkribus.eu/Get?id=SVZAIDARQWLMQTKQVIOOSAKV https://files.transkribus.eu/Get?id=XADEUHKOGGIWCOCZMTGGFHDP https://files.transkribus.eu/Get?id=QRUEXOAFCJBJXSXVTYKDCVKF https://files.transkribus.eu/Get?id=TZWAQKCGYPBWVZNSHVAKWCLZ https://files.transkribus.eu/Get?id=HVTGPLBHUEUXGJMGBNHIJIVS https://files.transkribus.eu/Get?id=KGTWYGJEQVUTLYQJJHDYRSDR https://files.transkribus.eu/Get?id=UCCYYBWUNOZJTXPTLDSGMKED https://files.transkribus.eu/Get?id=FTRLAADUOFFQLRZKSCULMDKF https://files.transkribus.eu/Get?id=BYLJWEGQKLYXLDPIEERCHAHG https://files.transkribus.eu/Get?id=SYZATLLCHFOLZWDODQRXNKNF https://files.transkribus.eu/Get?id=WZJQYUAGMQINLJKWSLBKSGTG https://files.transkribus.eu/Get?id=WOTWYEYWCOLUIZDUZMKQOBPA https://files.transkribus.eu/Get?id=IZHOVYUENLZPLEPYGPDACJXR https://files.transkribus.eu/Get?id=ITMLHBAAKCXDNTLMKMXNWOVM https://files.transkribus.eu/Get?id=GTEWMTDGWLEQMNDTONNPWVAV https://files.transkribus.eu/Get?id=XHVQSIUMEGUOGCMLVIBNPDIZ https://files.transkribus.eu/Get?id=IYDRMOZDVMAEHZLCLYWJFKZU https://files.transkribus.eu/Get?id=OZGAGQKATLEVBZMKNPUSZJZA https://files.transkribus.eu/Get?id=BMYKTWTVVTYYYQQSTXWQHKLC https://files.transkribus.eu/Get?id=IYUGXNCSKYCGDZZATBMXIPRX https://files.transkribus.eu/Get?id=BBTDFLKQWLYLTYYEMEYWRCFX
# Get XML files.
wget --content-disposition https://files.transkribus.eu/Get?id=JHVUSEFVRTWKTDYBEFVBEPFB https://files.transkribus.eu/Get?id=BYFBODHLFRJDQBVOROTLEWET https://files.transkribus.eu/Get?id=RWSSDKJHNAPTDXVLREBGBCLW https://files.transkribus.eu/Get?id=YEPDJBAQSQUMXTBRNXGGFREU https://files.transkribus.eu/Get?id=FZQWWFZCNWCYAPQRVIZTAOSX https://files.transkribus.eu/Get?id=IMXDAERWVVGYBIOMIEFTXFMK https://files.transkribus.eu/Get?id=LVZAFONLFVUFHYVDCAHAERYP https://files.transkribus.eu/Get?id=CIMPTJQYTQESVVGCBQRALMEX https://files.transkribus.eu/Get?id=CVSIQYLKNTXSVXRHBRBHIKKM https://files.transkribus.eu/Get?id=UIOFSVNICSMOSBKPJBZOATHM https://files.transkribus.eu/Get?id=NIEOIIUKDXUNFYJHHXADOUZH https://files.transkribus.eu/Get?id=RECFLPURJRTWVCNLRPARZWCT https://files.transkribus.eu/Get?id=BIIMISGELCYEAVCSFBEOBEXE https://files.transkribus.eu/Get?id=YSCXXSWLKELAWMDBRLAXKZPM https://files.transkribus.eu/Get?id=PPKQXCNQTIPCWWKKYEYKKFWR https://files.transkribus.eu/Get?id=TMGWJSEMDVZZUHIMCJMBQSAS https://files.transkribus.eu/Get?id=RQQGZJRNLALMLBHXWZAQRJJB https://files.transkribus.eu/Get?id=NKRITEGYTBOJEQKHIYFVPIKG https://files.transkribus.eu/Get?id=OHMVVRMMPUDTYDRDFHJZPKHZ https://files.transkribus.eu/Get?id=IQWIGWEOZAOZGEBTROXULIFN https://files.transkribus.eu/Get?id=XKLRDXMZFCGBBDKXXWQUOGTP https://files.transkribus.eu/Get?id=KBBFFENIEDVCCEJKGINYYOIA https://files.transkribus.eu/Get?id=VHHUSIBLNZVDYJDEUGTFNFIB https://files.transkribus.eu/Get?id=NWOLIMGYYSFJGHASEFSAHPSU https://files.transkribus.eu/Get?id=CMXZGEPQCEQPOYXCVMUAAMXL https://files.transkribus.eu/Get?id=UJMUBEOLKJSRNPSVFBSPIIQE https://files.transkribus.eu/Get?id=QNKLLUDVYNWHQFYPDGDKOSWG https://files.transkribus.eu/Get?id=UBELSVTLYVTCJSWPBNAKXCWN https://files.transkribus.eu/Get?id=UAQBIBAIRXGBSPALZPERMJQN https://files.transkribus.eu/Get?id=ABGYGICZRGMJPDKBTQRFMZWY https://files.transkribus.eu/Get?id=IEOAQAMGVZGEDZPSJCWPXJOW 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https://files.transkribus.eu/Get?id=QKGCVEHTCAHRULIYFKOKKCFY https://files.transkribus.eu/Get?id=KHQDPSYPSBRGAJUFRJMOYYHV https://files.transkribus.eu/Get?id=VKWKDCOHWOCNAROYRJKHLFYC https://files.transkribus.eu/Get?id=XNLDKCGFMCHVEZTAJQLOHVYS https://files.transkribus.eu/Get?id=LSUTSNAWNILOCLCJLJVHHPPP https://files.transkribus.eu/Get?id=VBICXRUQAIGVEXAVMWTWIUDK https://files.transkribus.eu/Get?id=KJFZNMXHNIVVXNRWRLNPFOOT https://files.transkribus.eu/Get?id=JBOQSXZLWOLKVMLXOVQDOKJU https://files.transkribus.eu/Get?id=IMDEJHWZKACFXIGKEFXPQLII https://files.transkribus.eu/Get?id=QZKWGDEXJPZKTDUCDFWDEOET https://files.transkribus.eu/Get?id=EQSKRRFMYFDNFAJKYSBEAGQZ https://files.transkribus.eu/Get?id=ECTQOJPXUKYFSXVWRRSGVQRO https://files.transkribus.eu/Get?id=XRDHUZNHGKFBUMJFWMZSNYGM https://files.transkribus.eu/Get?id=VXBCUSYDWISJNFPVLOPGMHQL https://files.transkribus.eu/Get?id=HASZIIPNMFNRBGRFNAHLLUBR https://files.transkribus.eu/Get?id=MUKOMKPEEGXLSMZSNNZVZQVF https://files.transkribus.eu/Get?id=TEYUPVVQHWBKCNMEBDOYBTNY https://files.transkribus.eu/Get?id=YYEOVUJKGVARJPNSIFGLTJDA https://files.transkribus.eu/Get?id=OZBOZQQGNFNCAIZRFBXBZTQU https://files.transkribus.eu/Get?id=SPDIYTHJUBLTXUFRDZHXVVNF https://files.transkribus.eu/Get?id=GBDRCZDQRFRWUHIBQMQQFWOB https://files.transkribus.eu/Get?id=PUXPSBYVMNNKUVFDVBXBUFGU https://files.transkribus.eu/Get?id=VWQGXYKCTNVODXWYFXCXIESE https://files.transkribus.eu/Get?id=QAGEQHUIBPCKCVZKCAYSKJAS https://files.transkribus.eu/Get?id=DXHLEYZKFOUPCGDJKILGLEEN https://files.transkribus.eu/Get?id=XJIGEZRWLZCHXNFMCJVGRJYO https://files.transkribus.eu/Get?id=CDFNKPNPFNLUDLMHNSVWIOJY https://files.transkribus.eu/Get?id=GMJIJQMKUDSKYOZBYNUSAXNB https://files.transkribus.eu/Get?id=ZIKEBIHOCZEPWRSXGLQIFANV https://files.transkribus.eu/Get?id=UMXWNECUPMBVUSYXQSQTLJXA https://files.transkribus.eu/Get?id=DOYOKEEZUPSEPOLQFFOSDXLI https://files.transkribus.eu/Get?id=BXVPCUAOJBDIXZTHJNVJQUVG https://files.transkribus.eu/Get?id=ZKGAJZFHGCNQPJACKBVUONZZ https://files.transkribus.eu/Get?id=KAAROZFEMHTPFBBZRROKOWBF https://files.transkribus.eu/Get?id=KGDGOZQBVHZIPFABKPUYMOPG https://files.transkribus.eu/Get?id=QEJFWXTMXGFJTFWCORHVCGCR https://files.transkribus.eu/Get?id=LPDOYQEQTNASZIWTBRVUZILS https://files.transkribus.eu/Get?id=YLQKKITJQKAOCOHWYOTYZUMC https://files.transkribus.eu/Get?id=LOTMIHEOLSGYTTJQPJCUDXIF https://files.transkribus.eu/Get?id=NLDJIZLJUKUYPWQZHQPRBACG https://files.transkribus.eu/Get?id=PNLSPNFUBYEFSRAMKEGHZRMX https://files.transkribus.eu/Get?id=TFLTRJJTYRGVLRUZKWXQJTSL https://files.transkribus.eu/Get?id=IGKKGDXZEUPHQEHRNVGGGSHF https://files.transkribus.eu/Get?id=KDNTAWHVDCRMWUJYHCXXNAEJ https://files.transkribus.eu/Get?id=CJNSNRLHKBUNFWVVDPFBOYGC https://files.transkribus.eu/Get?id=OZJOAVTDJANNGFWRCSPSSMNT https://files.transkribus.eu/Get?id=ZAKYZRJPMEUGAFMEETNWJHYD https://files.transkribus.eu/Get?id=AAKZVULGHVARRSMQCUZFRQOT https://files.transkribus.eu/Get?id=ANJKLTOEBRJAOFAZIQAOPDVL https://files.transkribus.eu/Get?id=QQENWYLYGEORJOOWXQGDDOYQ https://files.transkribus.eu/Get?id=RWKPNTKIKQTIMRNWWYGVBHXM https://files.transkribus.eu/Get?id=IAOLLMALNIHPDWGDSPCAKUFB https://files.transkribus.eu/Get?id=TOZWZVEROLTDQSSGQVVIDPFW https://files.transkribus.eu/Get?id=NZAQUQJMEOMMALKIAJTKYCEB https://files.transkribus.eu/Get?id=IYTYIEJULURSVZXZJOKNLGTP https://files.transkribus.eu/Get?id=PDDUAQELWDADQVLKYKTMUIEO
cd ..
mkdir Validation
cd Validation
# Get image files.
wget --content-disposition https://files.transkribus.eu/Get?id=VKHSBYKDRSPCXNYVVDUUJHIJ https://files.transkribus.eu/Get?id=ZFDKMFYHZSSWJRWJDBSKXLME https://files.transkribus.eu/Get?id=KUNKUUMWRVICWVMISJETKKSP https://files.transkribus.eu/Get?id=VCYDPLIKHZNSMBXZNEARDMQG https://files.transkribus.eu/Get?id=DSZCVDYESIQHIKUWNBMEGUMI https://files.transkribus.eu/Get?id=UMTPCFPNQPEMZGGSGOZWRHZZ https://files.transkribus.eu/Get?id=XGLLYKKRWGKDJTSTQZTQSBDB https://files.transkribus.eu/Get?id=GMDPHGGENKKZXGUNAYDUYWLO https://files.transkribus.eu/Get?id=YWGNYDFWVHJLFFSYFHFOPPWV https://files.transkribus.eu/Get?id=IVUIBDCTBUYBGAEWVULXXFST https://files.transkribus.eu/Get?id=SQCNQZEQTSUHTQWYPFYQALUH https://files.transkribus.eu/Get?id=SCOYGUKQFUEPENAKBDSOXYGF https://files.transkribus.eu/Get?id=UHGKWLEZJGEGEUDTJWHQHWGT https://files.transkribus.eu/Get?id=RLXCXHKMEZPDOPKXEQWALGPY https://files.transkribus.eu/Get?id=REUHQRVBWHKVAGYKNOBKCSNP https://files.transkribus.eu/Get?id=FARONPPATEJYGRXVRHXRBPPI https://files.transkribus.eu/Get?id=BYXLCAPXMRURUGUHGOCDUAWJ https://files.transkribus.eu/Get?id=RHZLBFIAGXPNVSEKDMXVNAZT https://files.transkribus.eu/Get?id=HMXEFFBXKKVTASJBJNLMYPNL https://files.transkribus.eu/Get?id=JRNXMKFSVZEOMVCMPGWFRPYJ https://files.transkribus.eu/Get?id=NTGOQDNHUKAKGEUWVHRHDUOR https://files.transkribus.eu/Get?id=JJLLOATKBIRFPVVUMQETOTTN https://files.transkribus.eu/Get?id=CAOYGGOXCZVJYMBKTZGNCFCT https://files.transkribus.eu/Get?id=WSAKKKUEQCSGPKCLCVHHCKVD
# Get XML files.
wget --content-disposition https://files.transkribus.eu/Get?id=CHDVOIIYLHWZEPXMAUJWWNKU https://files.transkribus.eu/Get?id=ZRLEVECHRBPRETNOKXOFWGPU https://files.transkribus.eu/Get?id=WCIRBUXVTCXPGZTDSGJLGVWR https://files.transkribus.eu/Get?id=LMXKCJUHLQTOTWZKQOYBXRRH https://files.transkribus.eu/Get?id=RVNPPKIXIUTUFYAYGDUMWPRB https://files.transkribus.eu/Get?id=BOCCILWYTULGOIMRGCUHLLBF https://files.transkribus.eu/Get?id=PHXCDTHIHWHPCPVZANZLMEZW https://files.transkribus.eu/Get?id=ERCWXYVDDXSQTNDNRADLLNSL https://files.transkribus.eu/Get?id=VWCEXDWQJWMZPPBWPGVUFLSU https://files.transkribus.eu/Get?id=REJFIMVSKOBVFYCGVPRYXSIL https://files.transkribus.eu/Get?id=QPDBOUSCBUNSAVIKCHKWKLOP https://files.transkribus.eu/Get?id=XVCPFSVVGHVLQYTRGUPECDBO https://files.transkribus.eu/Get?id=KAHBRRJHNECLTFIDYKEVHIKN https://files.transkribus.eu/Get?id=WPWCKGCRUCANVXXOAXMMMJHM https://files.transkribus.eu/Get?id=XQUUXHYOWQGAUAQIYITHIMBX https://files.transkribus.eu/Get?id=XJYJHKBQWTLIIBGYXZYMOMTE https://files.transkribus.eu/Get?id=RWJTCQCXVXQOTBIMHLYRZXYN https://files.transkribus.eu/Get?id=JBLGRLYTPUUOYLKCPNNSMGCY https://files.transkribus.eu/Get?id=AYSTOXAKCOUKGCLBAGEDTBQN https://files.transkribus.eu/Get?id=KRNQGZWNGROPPIIJXPATHOHH https://files.transkribus.eu/Get?id=QDARVKDOIPYISUFZBCOTHCXV https://files.transkribus.eu/Get?id=EMFJCMSQWEELQTLIQDVDHBHJ https://files.transkribus.eu/Get?id=FDIATJURTJQLLGMJTHTQIQTC https://files.transkribus.eu/Get?id=CUMMKSQMFFWKWEEBFRYKWNRL
cd ..

Install kraken

python3.9 -m venv venv3.9
source venv3.9/bin/activate
pip install -U pip setuptools wheel
pip install kraken
pip install -U torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113

Run training

The training is running on a Debian GNU Linux server with Nvidia RTX A5000 GPU.

(venv3.9) stweil@ocr-01:~/src/gitlab/scripta/escriptorium/Juristische_Konsilien_Tuebingen/Transkribus_Exporte$ time nice ketos train -f page -t list.train -e list.eval -o Juristische_Konsilien_Tuebingen -d cuda:0 --lag 20 -r 0.0001 -B 1 -w 0 -s '[1,120,0,1 Cr3,13,32 Do0.1,2 Mp2,2 Cr3,13,32 Do0.1,2 Mp2,2 Cr3,9,64 Do0.1,2 Mp2,2 Cr3,9,64 Do0.1,2 S1(1x0)1,3 Lbx200 Do0.1,2 Lbx200 Do.1,2 Lbx200 Do]'
WARNING:root:Torch version 1.11.0+cu113 has not been tested with coremltools. You may run into unexpected errors. Torch 1.10.2 is the most recent version that has been tested.
[05/24/22 17:38:52] WARNING  alphabet mismatch: chars in training set only: {'โ€ก', '[', 'โ€ ', '๊Ÿ', '=', 'รป', '๊ธ', 'ยบ', 'X', 'โ•’', 'รœ', 'โ€™', 'โ™ƒ', 'ยฝ', ']',   train.py:304
                             '๊ฏ'} (not included in accuracy test during training)                                                                                     
                    WARNING  alphabet mismatch: chars in validation set only: {'ร„', 'รน', 'อฆ'} (not trained)                                                train.py:308
Trainer already configured with model summary callbacks: [<class 'pytorch_lightning.callbacks.rich_model_summary.RichModelSummary'>]. Skipping setting a default `ModelSummary` callback.
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(val_check_interval=1.0)` was configured so validation will run at the end of the training epoch..
[05/24/22 17:38:54] WARNING  Non-encodable sequence รนltis... encountered. Advancing one code point.                                                       codec.py:131
                    WARNING  Non-encodable sequence ร„rโ€ž... encountered. Advancing one code point.                                                         codec.py:131
                    WARNING  Non-encodable sequence อฆ 166... encountered. Advancing one code point.                                                        codec.py:131
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”“
โ”ƒ    โ”ƒ Name      โ”ƒ Type                     โ”ƒ Params โ”ƒ
โ”กโ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ
โ”‚ 0  โ”‚ net       โ”‚ MultiParamSequential     โ”‚  4.0 M โ”‚
โ”‚ 1  โ”‚ net.C_0   โ”‚ ActConv2D                โ”‚  1.3 K โ”‚
โ”‚ 2  โ”‚ net.Do_1  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 3  โ”‚ net.Mp_2  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 4  โ”‚ net.C_3   โ”‚ ActConv2D                โ”‚ 40.0 K โ”‚
โ”‚ 5  โ”‚ net.Do_4  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 6  โ”‚ net.Mp_5  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 7  โ”‚ net.C_6   โ”‚ ActConv2D                โ”‚ 55.4 K โ”‚
โ”‚ 8  โ”‚ net.Do_7  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 9  โ”‚ net.Mp_8  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 10 โ”‚ net.C_9   โ”‚ ActConv2D                โ”‚  110 K โ”‚
โ”‚ 11 โ”‚ net.Do_10 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 12 โ”‚ net.S_11  โ”‚ Reshape                  โ”‚      0 โ”‚
โ”‚ 13 โ”‚ net.L_12  โ”‚ TransposedSummarizingRNN โ”‚  1.9 M โ”‚
โ”‚ 14 โ”‚ net.Do_13 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 15 โ”‚ net.L_14  โ”‚ TransposedSummarizingRNN โ”‚  963 K โ”‚
โ”‚ 16 โ”‚ net.Do_15 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 17 โ”‚ net.L_16  โ”‚ TransposedSummarizingRNN โ”‚  963 K โ”‚
โ”‚ 18 โ”‚ net.Do_17 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 19 โ”‚ net.O_18  โ”‚ LinSoftmax               โ”‚ 48.5 K โ”‚
โ””โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
Trainable params: 4.0 M                                                                                                                                               
Non-trainable params: 0                                                                                                                                               
Total params: 4.0 M                                                                                                                                                   
Total estimated model params size (MB): 16                                                                                                                            
stage 0/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:58 val_accuracy: 0.00000  early_stopping: 0/20 0.00000
stage 1/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:57 val_accuracy: 0.27963  early_stopping: 0/20 0.27963
stage 2/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:44 val_accuracy: 0.63872  early_stopping: 0/20 0.63872
stage 3/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:06:02 val_accuracy: 0.77172  early_stopping: 0/20 0.77172
stage 4/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:59 val_accuracy: 0.82858  early_stopping: 0/20 0.82858
stage 5/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:06:05 val_accuracy: 0.86218  early_stopping: 0/20 0.86218
stage 6/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:50 val_accuracy: 0.88192  early_stopping: 0/20 0.88192
stage 7/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:06:01 val_accuracy: 0.89528  early_stopping: 0/20 0.89528
stage 8/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:55 val_accuracy: 0.90674  early_stopping: 0/20 0.90674
stage 9/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:06:02 val_accuracy: 0.90923  early_stopping: 0/20 0.90923
stage 10/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:06:03 val_accuracy: 0.91599  early_stopping: 0/20 0.91599
stage 11/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:48 val_accuracy: 0.91963  early_stopping: 0/20 0.91963
stage 12/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:42 val_accuracy: 0.92259  early_stopping: 0/20 0.92259
stage 13/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:35 val_accuracy: 0.92418  early_stopping: 0/20 0.92418
stage 14/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:59 val_accuracy: 0.92860  early_stopping: 0/20 0.92860
stage 15/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:46 val_accuracy: 0.92850  early_stopping: 1/20 0.92860
stage 16/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:48 val_accuracy: 0.92978  early_stopping: 0/20 0.92978
stage 17/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:49 val_accuracy: 0.93181  early_stopping: 0/20 0.93181
stage 18/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:06:02 val_accuracy: 0.93016  early_stopping: 1/20 0.93181
stage 19/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:55 val_accuracy: 0.93448  early_stopping: 0/20 0.93448
stage 20/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:33 val_accuracy: 0.93439  early_stopping: 1/20 0.93448
stage 21/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:27 val_accuracy: 0.93539  early_stopping: 0/20 0.93539
stage 22/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:33 val_accuracy: 0.93567  early_stopping: 0/20 0.93567
stage 23/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:32 val_accuracy: 0.93588  early_stopping: 0/20 0.93588
stage 24/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:39 val_accuracy: 0.93722  early_stopping: 0/20 0.93722
stage 25/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:41 val_accuracy: 0.93679  early_stopping: 1/20 0.93722
stage 26/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:31 val_accuracy: 0.93707  early_stopping: 2/20 0.93722
stage 27/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:34 val_accuracy: 0.93847  early_stopping: 0/20 0.93847
stage 28/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:32 val_accuracy: 0.93747  early_stopping: 1/20 0.93847
stage 29/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:42 val_accuracy: 0.93884  early_stopping: 0/20 0.93884
stage 30/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:23 val_accuracy: 0.93884  early_stopping: 1/20 0.93884
stage 31/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:21 val_accuracy: 0.93682  early_stopping: 2/20 0.93884
stage 32/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:44 val_accuracy: 0.93869  early_stopping: 3/20 0.93884
stage 33/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:49 val_accuracy: 0.93657  early_stopping: 4/20 0.93884
stage 34/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:37 val_accuracy: 0.93919  early_stopping: 0/20 0.93919
stage 35/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:39 val_accuracy: 0.93782  early_stopping: 1/20 0.93919
stage 36/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:04:27 val_accuracy: 0.93875  early_stopping: 2/20 0.93919
stage 37/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:49 val_accuracy: 0.94028  early_stopping: 0/20 0.94028
stage 38/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:05:50 val_accuracy: 0.94112  early_stopping: 0/20 0.94112
stage 39/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:06:05 val_accuracy: 0.93978  early_stopping: 1/20 0.94112
stage 40/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:08:45 val_accuracy: 0.93922  early_stopping: 2/20 0.94112
stage 41/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:09:56 val_accuracy: 0.94046  early_stopping: 3/20 0.94112
stage 42/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:07:38 val_accuracy: 0.94186  early_stopping: 0/20 0.94186
stage 43/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:09:34 val_accuracy: 0.93940  early_stopping: 1/20 0.94186
stage 44/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:09:55 val_accuracy: 0.93909  early_stopping: 2/20 0.94186
stage 45/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 1:10:04 val_accuracy: 0.94040  early_stopping: 3/20 0.94186
stage 46/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•บโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 1930/7366 0:51:25 0:18:01 val_accuracy: 0.94040  early_stopping: 3/20 0.94186

Run training with augmentation

Using more than one worker process makes the training much faster, and augmentation should result in a better model.

pip install albumentations
(venv3.9) stweil@ocr-01:~/src/gitlab/scripta/escriptorium/Juristische_Konsilien_Tuebingen/Transkribus_Exporte$ time nice ketos train -f page -t list.train -e list.eval -o Juristische_Konsilien_Tuebingen+ -d cuda:0 --augment --workers 4 -r 0.0001 -B 1 --min-epochs 20 -w 0 -s '[1,120,0,1 Cr3,13,32 Do0.1,2 Mp2,2 Cr3,13,32 Do0.1,2 Mp2,2 Cr3,9,64 Do0.1,2 Mp2,2 Cr3,9,64 Do0.1,2 S1(1x0)1,3 Lbx200 Do0.1,2 Lbx200 Do.1,2 Lbx200 Do]'
WARNING:root:scikit-learn version 1.1.1 is not supported. Minimum required version: 0.17. Maximum required version: 0.19.2. Disabling scikit-learn conversion API.
WARNING:root:Torch version 1.11.0+cu113 has not been tested with coremltools. You may run into unexpected errors. Torch 1.10.2 is the most recent version that has been tested.
[05/26/22 13:43:36] WARNING  alphabet mismatch: chars in training set only: {'โ€™', '๊ฏ', 'รป', ']', '๊Ÿ', '๊ธ', 'รœ', '[', 'โ€ ', '=', 'โ€ก', 'ยบ', 'ยฝ', 'X', 'โ™ƒ', 'โ•’'} (not included in accuracy test during        train.py:304
                             training)                                                                                                                                                                                
                    WARNING  alphabet mismatch: chars in validation set only: {'อฆ', 'ร„', 'รน'} (not trained)                                                                                                train.py:308
Trainer already configured with model summary callbacks: [<class 'pytorch_lightning.callbacks.rich_model_summary.RichModelSummary'>]. Skipping setting a default `ModelSummary` callback.
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(val_check_interval=1.0)` was configured so validation will run at the end of the training epoch..
[05/26/22 13:43:38] WARNING  Non-encodable sequence รนltis... encountered. Advancing one code point.                                                                                                       codec.py:131
                    WARNING  Non-encodable sequence ร„rโ€ž... encountered. Advancing one code point.                                                                                                         codec.py:131
                    WARNING  Non-encodable sequence อฆ 166... encountered. Advancing one code point.                                                                                                        codec.py:131
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”“
โ”ƒ    โ”ƒ Name      โ”ƒ Type                     โ”ƒ Params โ”ƒ
โ”กโ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ
โ”‚ 0  โ”‚ net       โ”‚ MultiParamSequential     โ”‚  4.0 M โ”‚
โ”‚ 1  โ”‚ net.C_0   โ”‚ ActConv2D                โ”‚  1.3 K โ”‚
โ”‚ 2  โ”‚ net.Do_1  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 3  โ”‚ net.Mp_2  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 4  โ”‚ net.C_3   โ”‚ ActConv2D                โ”‚ 40.0 K โ”‚
โ”‚ 5  โ”‚ net.Do_4  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 6  โ”‚ net.Mp_5  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 7  โ”‚ net.C_6   โ”‚ ActConv2D                โ”‚ 55.4 K โ”‚
โ”‚ 8  โ”‚ net.Do_7  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 9  โ”‚ net.Mp_8  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 10 โ”‚ net.C_9   โ”‚ ActConv2D                โ”‚  110 K โ”‚
โ”‚ 11 โ”‚ net.Do_10 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 12 โ”‚ net.S_11  โ”‚ Reshape                  โ”‚      0 โ”‚
โ”‚ 13 โ”‚ net.L_12  โ”‚ TransposedSummarizingRNN โ”‚  1.9 M โ”‚
โ”‚ 14 โ”‚ net.Do_13 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 15 โ”‚ net.L_14  โ”‚ TransposedSummarizingRNN โ”‚  963 K โ”‚
โ”‚ 16 โ”‚ net.Do_15 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 17 โ”‚ net.L_16  โ”‚ TransposedSummarizingRNN โ”‚  963 K โ”‚
โ”‚ 18 โ”‚ net.Do_17 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 19 โ”‚ net.O_18  โ”‚ LinSoftmax               โ”‚ 48.5 K โ”‚
โ””โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
Trainable params: 4.0 M                                                                                                                                                                                               
Non-trainable params: 0                                                                                                                                                                                               
Total params: 4.0 M                                                                                                                                                                                                   
Total estimated model params size (MB): 16                                                                                                                                                                            
stage 0/โˆž  โ”โ”โ”โ•ธโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 673/7366 0:16:39 0:01:43  early_stopping: 0/5 -inf

Increasing the number of workers further reduces the time per epoch even more.

(venv3.9) stweil@ocr-01:~/src/gitlab/scripta/escriptorium/Juristische_Konsilien_Tuebingen/Transkribus_Exporte$ time nice ketos train -f page -t list.train -e list.eval -o Juristische_Konsilien_Tuebingen+ -d cuda:0 
--augment --workers 16 -r 0.0001 -B 1 --min-epochs 20 -w 0 -s '[1,120,0,1 Cr3,13,32 Do0.1,2 Mp2,2 Cr3,13,32 Do0.1,2 Mp2,2 Cr3,9,64 Do0.1,2 Mp2,2 Cr3,9,64 Do0.1,2 S1(1x0)1,3 Lbx200 Do0.1,2 Lbx200 Do.1,2 Lbx200 Do]'
WARNING:root:scikit-learn version 1.1.1 is not supported. Minimum required version: 0.17. Maximum required version: 0.19.2. Disabling scikit-learn conversion API.
WARNING:root:Torch version 1.11.0+cu113 has not been tested with coremltools. You may run into unexpected errors. Torch 1.10.2 is the most recent version that has been tested.
[05/26/22 13:49:52] WARNING  alphabet mismatch: chars in training set only: {'๊ฏ', 'ยฝ', '๊Ÿ', 'โ•’', '=', '[', 'รป', 'โ€™', 'รœ', 'โ€ ', 'โ™ƒ', '๊ธ', 'โ€ก', 'X', 'ยบ', ']'} (not included in accuracy test during        train.py:304
                             training)                                                                                                                                                                                
                    WARNING  alphabet mismatch: chars in validation set only: {'ร„', 'รน', 'อฆ'} (not trained)                                                                                                train.py:308
Trainer already configured with model summary callbacks: [<class 'pytorch_lightning.callbacks.rich_model_summary.RichModelSummary'>]. Skipping setting a default `ModelSummary` callback.
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(val_check_interval=1.0)` was configured so validation will run at the end of the training epoch..
[05/26/22 13:49:55] WARNING  Non-encodable sequence รนltis... encountered. Advancing one code point.                                                                                                       codec.py:131
                    WARNING  Non-encodable sequence ร„rโ€ž... encountered. Advancing one code point.                                                                                                         codec.py:131
                    WARNING  Non-encodable sequence อฆ 166... encountered. Advancing one code point.                                                                                                        codec.py:131
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”“
โ”ƒ    โ”ƒ Name      โ”ƒ Type                     โ”ƒ Params โ”ƒ
โ”กโ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ
โ”‚ 0  โ”‚ net       โ”‚ MultiParamSequential     โ”‚  4.0 M โ”‚
โ”‚ 1  โ”‚ net.C_0   โ”‚ ActConv2D                โ”‚  1.3 K โ”‚
โ”‚ 2  โ”‚ net.Do_1  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 3  โ”‚ net.Mp_2  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 4  โ”‚ net.C_3   โ”‚ ActConv2D                โ”‚ 40.0 K โ”‚
โ”‚ 5  โ”‚ net.Do_4  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 6  โ”‚ net.Mp_5  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 7  โ”‚ net.C_6   โ”‚ ActConv2D                โ”‚ 55.4 K โ”‚
โ”‚ 8  โ”‚ net.Do_7  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 9  โ”‚ net.Mp_8  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 10 โ”‚ net.C_9   โ”‚ ActConv2D                โ”‚  110 K โ”‚
โ”‚ 11 โ”‚ net.Do_10 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 12 โ”‚ net.S_11  โ”‚ Reshape                  โ”‚      0 โ”‚
โ”‚ 13 โ”‚ net.L_12  โ”‚ TransposedSummarizingRNN โ”‚  1.9 M โ”‚
โ”‚ 14 โ”‚ net.Do_13 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 15 โ”‚ net.L_14  โ”‚ TransposedSummarizingRNN โ”‚  963 K โ”‚
โ”‚ 16 โ”‚ net.Do_15 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 17 โ”‚ net.L_16  โ”‚ TransposedSummarizingRNN โ”‚  963 K โ”‚
โ”‚ 18 โ”‚ net.Do_17 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 19 โ”‚ net.O_18  โ”‚ LinSoftmax               โ”‚ 48.5 K โ”‚
โ””โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
Trainable params: 4.0 M                                                                                                                                                                                               
Non-trainable params: 0                                                                                                                                                                                               
Total params: 4.0 M                                                                                                                                                                                                   
Total estimated model params size (MB): 16                                                                                                                                                                            
stage 0/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:03 val_accuracy: 0.00000  early_stopping: 0/5 0.00000
stage 1/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.20782  early_stopping: 0/5 0.20782
stage 2/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.57978  early_stopping: 0/5 0.57978
stage 3/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.71006  early_stopping: 0/5 0.71006
stage 4/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:20 val_accuracy: 0.78446  early_stopping: 0/5 0.78446
stage 5/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.82139  early_stopping: 0/5 0.82139
stage 6/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:57 val_accuracy: 0.83555  early_stopping: 0/5 0.83555
stage 7/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.86224  early_stopping: 0/5 0.86224
stage 8/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.86741  early_stopping: 0/5 0.86741
stage 9/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.88089  early_stopping: 0/5 0.88089
stage 10/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.89192  early_stopping: 0/5 0.89192
stage 11/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.87604  early_stopping: 1/5 0.89192
stage 12/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.89733  early_stopping: 0/5 0.89733
stage 13/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.90216  early_stopping: 0/5 0.90216
stage 14/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.90222  early_stopping: 0/5 0.90222
stage 15/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.90182  early_stopping: 1/5 0.90222
stage 16/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:48 val_accuracy: 0.90608  early_stopping: 0/5 0.90608
stage 17/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:44 val_accuracy: 0.91259  early_stopping: 0/5 0.91259
stage 18/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.90496  early_stopping: 1/5 0.91259
stage 19/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.91141  early_stopping: 2/5 0.91259
stage 20/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.90126  early_stopping: 3/5 0.91259
stage 21/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.91119  early_stopping: 4/5 0.91259
stage 22/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.91100  early_stopping: 5/5 0.91259
Moving best model Juristische_Konsilien_Tuebingen+_17.mlmodel (0.9125926494598389) to Juristische_Konsilien_Tuebingen+_best.mlmodel

real    151m8.293s
user    2131m47.894s
sys     1193m39.862s

Resume the training:

(venv3.9) stweil@ocr-01:~/src/gitlab/scripta/escriptorium/Juristische_Konsilien_Tuebingen/Transkribus_Exporte$ time nice ketos train -i Juristische_Konsilien_Tuebingen+_17.mlmodel -f page -t list.train -e list.eval -o Juristische_Konsilien_Tuebingen+ -d cuda:0 --augment --workers 16 -r 0.0001 -B 1 --min-epochs 200 --lag 10 -w 0 -s '[1,120,0,1 Cr3,13,32 Do0.1,2 Mp2,2 Cr3,13,32 Do0.1,2 Mp2,2 Cr3,9,64 Do0.1,2 Mp2,2 Cr3,9,64 Do0
.1,2 S1(1x0)1,3 Lbx200 Do0.1,2 Lbx200 Do.1,2 Lbx200 Do]'
WARNING:root:scikit-learn version 1.1.1 is not supported. Minimum required version: 0.17. Maximum required version: 0.19.2. Disabling scikit-learn conversion API.
WARNING:root:Torch version 1.11.0+cu113 has not been tested with coremltools. You may run into unexpected errors. Torch 1.10.2 is the most recent version that has been tested.
[05/26/22 16:56:31] WARNING  alphabet mismatch: chars in training set only: {'รœ', 'โ€ก', 'รป', 'โ™ƒ', 'โ•’', '๊ฏ', 'ยบ', '๊ธ', 'โ€ ', '=', 'X', '[', 'ยฝ', ']', '๊Ÿ', 'โ€™'} (not included in accuracy test during        train.py:304
                             training)                                                                                                                                                                                
Trainer already configured with model summary callbacks: [<class 'pytorch_lightning.callbacks.rich_model_summary.RichModelSummary'>]. Skipping setting a default `ModelSummary` callback.
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(val_check_interval=1.0)` was configured so validation will run at the end of the training epoch..
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”“
โ”ƒ    โ”ƒ Name      โ”ƒ Type                     โ”ƒ Params โ”ƒ
โ”กโ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ
โ”‚ 0  โ”‚ net       โ”‚ MultiParamSequential     โ”‚  4.0 M โ”‚
โ”‚ 1  โ”‚ net.C_0   โ”‚ ActConv2D                โ”‚  1.3 K โ”‚
โ”‚ 2  โ”‚ net.Do_1  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 3  โ”‚ net.Mp_2  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 4  โ”‚ net.C_3   โ”‚ ActConv2D                โ”‚ 40.0 K โ”‚
โ”‚ 5  โ”‚ net.Do_4  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 6  โ”‚ net.Mp_5  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 7  โ”‚ net.C_6   โ”‚ ActConv2D                โ”‚ 55.4 K โ”‚
โ”‚ 8  โ”‚ net.Do_7  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 9  โ”‚ net.Mp_8  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 10 โ”‚ net.C_9   โ”‚ ActConv2D                โ”‚  110 K โ”‚
โ”‚ 11 โ”‚ net.Do_10 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 12 โ”‚ net.S_11  โ”‚ Reshape                  โ”‚      0 โ”‚
โ”‚ 13 โ”‚ net.L_12  โ”‚ TransposedSummarizingRNN โ”‚  1.9 M โ”‚
โ”‚ 14 โ”‚ net.Do_13 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 15 โ”‚ net.L_14  โ”‚ TransposedSummarizingRNN โ”‚  963 K โ”‚
โ”‚ 16 โ”‚ net.Do_15 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 17 โ”‚ net.L_16  โ”‚ TransposedSummarizingRNN โ”‚  963 K โ”‚
โ”‚ 18 โ”‚ net.Do_17 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 19 โ”‚ net.O_18  โ”‚ LinSoftmax               โ”‚ 48.5 K โ”‚
โ””โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
Trainable params: 4.0 M                                                                                                                                                                                               
Non-trainable params: 0                                                                                                                                                                                               
Total params: 4.0 M                                                                                                                                                                                                   
Total estimated model params size (MB): 16                                                                                                                                                                            
stage 0/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:05 val_accuracy: 0.91661  early_stopping: 0/10 0.91661
stage 1/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.89668  early_stopping: 1/10 0.91661
stage 2/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.91540  early_stopping: 2/10 0.91661
stage 3/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.92044  early_stopping: 0/10 0.92044
stage 4/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:19 val_accuracy: 0.90824  early_stopping: 1/10 0.92044
stage 5/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.91755  early_stopping: 2/10 0.92044
stage 6/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.90014  early_stopping: 3/10 0.92044
stage 7/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.91244  early_stopping: 4/10 0.92044
stage 8/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.91060  early_stopping: 5/10 0.92044
stage 9/โˆž  โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:02 val_accuracy: 0.91213  early_stopping: 6/10 0.92044
stage 10/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.90397  early_stopping: 7/10 0.92044
stage 11/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92128  early_stopping: 0/10 0.92128
stage 12/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:29 val_accuracy: 0.90992  early_stopping: 1/10 0.92128
stage 13/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.92181  early_stopping: 0/10 0.92181
stage 14/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.91278  early_stopping: 1/10 0.92181
stage 15/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92343  early_stopping: 0/10 0.92343
stage 16/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.92577  early_stopping: 0/10 0.92577
stage 17/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.92166  early_stopping: 1/10 0.92577
stage 18/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.92197  early_stopping: 2/10 0.92577
stage 19/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.92240  early_stopping: 3/10 0.92577
stage 20/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:57 val_accuracy: 0.91297  early_stopping: 4/10 0.92577
stage 21/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92078  early_stopping: 5/10 0.92577
stage 22/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92418  early_stopping: 6/10 0.92577
stage 23/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.92611  early_stopping: 0/10 0.92611
stage 24/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.91758  early_stopping: 1/10 0.92611
stage 25/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.92412  early_stopping: 2/10 0.92611
stage 26/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.91991  early_stopping: 3/10 0.92611
stage 27/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.92287  early_stopping: 4/10 0.92611
stage 28/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.92069  early_stopping: 5/10 0.92611
stage 29/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92767  early_stopping: 0/10 0.92767
stage 30/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:53 val_accuracy: 0.92234  early_stopping: 1/10 0.92767
stage 31/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:41 val_accuracy: 0.92848  early_stopping: 0/10 0.92848
stage 32/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.91652  early_stopping: 1/10 0.92848
stage 33/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.92704  early_stopping: 2/10 0.92848
stage 34/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93280  early_stopping: 0/10 0.93280
stage 35/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.91870  early_stopping: 1/10 0.93280
stage 36/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92726  early_stopping: 2/10 0.93280
stage 37/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92041  early_stopping: 3/10 0.93280
stage 38/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.92673  early_stopping: 4/10 0.93280
stage 39/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92894  early_stopping: 5/10 0.93280
stage 40/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.92633  early_stopping: 6/10 0.93280
stage 41/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:03 val_accuracy: 0.92695  early_stopping: 7/10 0.93280
stage 42/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.91484  early_stopping: 8/10 0.93280
stage 43/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92997  early_stopping: 9/10 0.93280
stage 44/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93296  early_stopping: 0/10 0.93296
stage 45/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.91892  early_stopping: 1/10 0.93296
stage 46/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.91798  early_stopping: 2/10 0.93296
stage 47/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92888  early_stopping: 3/10 0.93296
stage 48/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.93000  early_stopping: 4/10 0.93296
stage 49/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92135  early_stopping: 5/10 0.93296
stage 50/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92256  early_stopping: 6/10 0.93296
stage 51/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.92975  early_stopping: 7/10 0.93296
stage 52/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:03 val_accuracy: 0.92468  early_stopping: 8/10 0.93296
stage 53/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:29 val_accuracy: 0.92589  early_stopping: 9/10 0.93296
stage 54/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.92589  early_stopping: 9/10 0.93296Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 54/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.92265  early_stopping: 10/10 0.93296
stage 55/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92265  early_stopping: 10/10 0.93296Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 55/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92901  early_stopping: 11/10 0.93296
stage 56/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92901  early_stopping: 11/10 0.93296Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 56/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92493  early_stopping: 12/10 0.93296
stage 57/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.92493  early_stopping: 12/10 0.93296Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 57/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.92159  early_stopping: 13/10 0.93296
stage 58/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.92159  early_stopping: 13/10 0.93296Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 58/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.92097  early_stopping: 14/10 0.93296
stage 59/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.92097  early_stopping: 14/10 0.93296Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 59/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.92620  early_stopping: 15/10 0.93296
stage 60/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93495  early_stopping: 0/10 0.93495
stage 61/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.92433  early_stopping: 1/10 0.93495
stage 62/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92957  early_stopping: 2/10 0.93495
stage 63/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:59 val_accuracy: 0.92882  early_stopping: 3/10 0.93495
stage 64/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.92863  early_stopping: 4/10 0.93495
stage 65/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93377  early_stopping: 5/10 0.93495
stage 66/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.93218  early_stopping: 6/10 0.93495
stage 67/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93744  early_stopping: 0/10 0.93744
stage 68/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92683  early_stopping: 1/10 0.93744
stage 69/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93293  early_stopping: 2/10 0.93744
stage 70/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93520  early_stopping: 3/10 0.93744
stage 71/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.93246  early_stopping: 4/10 0.93744
stage 72/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92340  early_stopping: 5/10 0.93744
stage 73/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:10 val_accuracy: 0.93548  early_stopping: 6/10 0.93744
stage 74/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:16 val_accuracy: 0.92944  early_stopping: 7/10 0.93744
stage 75/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93078  early_stopping: 8/10 0.93744
stage 76/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93094  early_stopping: 9/10 0.93744
stage 77/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93094  early_stopping: 9/10 0.93744Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 77/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.92941  early_stopping: 10/10 0.93744
stage 78/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92941  early_stopping: 10/10 0.93744Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 78/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92044  early_stopping: 11/10 0.93744
stage 79/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.92044  early_stopping: 11/10 0.93744Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 79/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93489  early_stopping: 12/10 0.93744
stage 80/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93489  early_stopping: 12/10 0.93744Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 80/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.92359  early_stopping: 13/10 0.93744
stage 81/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93751  early_stopping: 0/10 0.93751
stage 82/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92848  early_stopping: 1/10 0.93751
stage 83/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.93937  early_stopping: 0/10 0.93937
stage 84/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:00 val_accuracy: 0.92841  early_stopping: 1/10 0.93937
stage 85/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93716  early_stopping: 2/10 0.93937
stage 86/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92876  early_stopping: 3/10 0.93937
stage 87/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92412  early_stopping: 4/10 0.93937
stage 88/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93461  early_stopping: 5/10 0.93937
stage 89/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.92627  early_stopping: 6/10 0.93937
stage 90/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93081  early_stopping: 7/10 0.93937
stage 91/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93542  early_stopping: 8/10 0.93937
stage 92/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.93000  early_stopping: 9/10 0.93937
stage 93/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:29 val_accuracy: 0.93000  early_stopping: 9/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 93/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:29 val_accuracy: 0.93667  early_stopping: 10/10 0.93937
stage 94/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:29 val_accuracy: 0.93667  early_stopping: 10/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 94/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:29 val_accuracy: 0.93150  early_stopping: 11/10 0.93937
stage 95/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:03 val_accuracy: 0.93150  early_stopping: 11/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 95/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:03 val_accuracy: 0.93486  early_stopping: 12/10 0.93937
stage 96/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93486  early_stopping: 12/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 96/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93673  early_stopping: 13/10 0.93937
stage 97/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.93673  early_stopping: 13/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 97/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.93623  early_stopping: 14/10 0.93937
stage 98/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:28 val_accuracy: 0.93623  early_stopping: 14/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 98/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:28 val_accuracy: 0.91889  early_stopping: 15/10 0.93937
stage 99/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.91889  early_stopping: 15/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 99/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.93053  early_stopping: 16/10 0.93937
stage 100/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93053  early_stopping: 16/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 100/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93377  early_stopping: 17/10 0.93937
stage 101/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93377  early_stopping: 17/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 101/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93196  early_stopping: 18/10 0.93937
stage 102/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93196  early_stopping: 18/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 102/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93358  early_stopping: 19/10 0.93937
stage 103/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.93358  early_stopping: 19/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 103/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.93595  early_stopping: 20/10 0.93937
stage 104/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93595  early_stopping: 20/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 104/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.92745  early_stopping: 21/10 0.93937
stage 105/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:45 val_accuracy: 0.92745  early_stopping: 21/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 105/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:45 val_accuracy: 0.93128  early_stopping: 22/10 0.93937
stage 106/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:50 val_accuracy: 0.93128  early_stopping: 22/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 106/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:50 val_accuracy: 0.92026  early_stopping: 23/10 0.93937
stage 107/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.92026  early_stopping: 23/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 107/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.93916  early_stopping: 24/10 0.93937
stage 108/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93916  early_stopping: 24/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 108/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93274  early_stopping: 25/10 0.93937
stage 109/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93274  early_stopping: 25/10 0.93937Trainer was signaled to stop but required minimum epochs (200) or minimum steps (None) has not been met. Training will continue...
stage 109/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.92549  early_stopping: 26/10 0.93937
stage 110/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93635  early_stopping: 27/10 0.93937
stage 111/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93589  early_stopping: 28/10 0.93937
stage 112/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92695  early_stopping: 29/10 0.93937
stage 113/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93414  early_stopping: 30/10 0.93937
stage 114/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.92760  early_stopping: 31/10 0.93937
stage 115/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93772  early_stopping: 32/10 0.93937
stage 116/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:10 val_accuracy: 0.93368  early_stopping: 33/10 0.93937
stage 117/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93947  early_stopping: 0/10 0.93947
stage 118/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.93816  early_stopping: 1/10 0.93947
stage 119/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93732  early_stopping: 2/10 0.93947
stage 120/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.92882  early_stopping: 3/10 0.93947
stage 121/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.93084  early_stopping: 4/10 0.93947
stage 122/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93284  early_stopping: 5/10 0.93947
stage 123/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93271  early_stopping: 6/10 0.93947
stage 124/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.93389  early_stopping: 7/10 0.93947
stage 125/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92788  early_stopping: 8/10 0.93947
stage 126/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.94028  early_stopping: 0/10 0.94028
stage 127/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:04 val_accuracy: 0.93582  early_stopping: 1/10 0.94028
stage 128/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.92848  early_stopping: 2/10 0.94028
stage 129/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.93598  early_stopping: 3/10 0.94028
stage 130/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92518  early_stopping: 4/10 0.94028
stage 131/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93461  early_stopping: 5/10 0.94028
stage 132/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92764  early_stopping: 6/10 0.94028
stage 133/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.94162  early_stopping: 0/10 0.94162
stage 134/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.94411  early_stopping: 0/10 0.94411
stage 135/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93835  early_stopping: 1/10 0.94411
stage 136/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:28 val_accuracy: 0.93794  early_stopping: 2/10 0.94411
stage 137/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.94000  early_stopping: 3/10 0.94411
stage 138/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:57 val_accuracy: 0.93875  early_stopping: 4/10 0.94411
stage 139/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93735  early_stopping: 5/10 0.94411
stage 140/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93894  early_stopping: 6/10 0.94411
stage 141/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93327  early_stopping: 7/10 0.94411
stage 142/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93632  early_stopping: 8/10 0.94411
stage 143/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93729  early_stopping: 9/10 0.94411
stage 144/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.92988  early_stopping: 10/10 0.94411
stage 145/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.93181  early_stopping: 11/10 0.94411
stage 146/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:29 val_accuracy: 0.93701  early_stopping: 12/10 0.94411
stage 147/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.94056  early_stopping: 13/10 0.94411
stage 148/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:03 val_accuracy: 0.94401  early_stopping: 14/10 0.94411
stage 149/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:35 val_accuracy: 0.94548  early_stopping: 0/10 0.94548
stage 150/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.92340  early_stopping: 1/10 0.94548
stage 151/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93997  early_stopping: 2/10 0.94548
stage 152/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:29 val_accuracy: 0.93903  early_stopping: 3/10 0.94548
stage 153/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.93421  early_stopping: 4/10 0.94548
stage 154/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:31 val_accuracy: 0.93856  early_stopping: 5/10 0.94548
stage 155/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93860  early_stopping: 6/10 0.94548
stage 156/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.92138  early_stopping: 7/10 0.94548
stage 157/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93825  early_stopping: 8/10 0.94548
stage 158/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.93171  early_stopping: 9/10 0.94548
stage 159/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:59 val_accuracy: 0.93087  early_stopping: 10/10 0.94548
stage 160/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93695  early_stopping: 11/10 0.94548
stage 161/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.93364  early_stopping: 12/10 0.94548
stage 162/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.94330  early_stopping: 13/10 0.94548
stage 163/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93249  early_stopping: 14/10 0.94548
stage 164/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.94252  early_stopping: 15/10 0.94548
stage 165/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93324  early_stopping: 16/10 0.94548
stage 166/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.94255  early_stopping: 17/10 0.94548
stage 167/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93212  early_stopping: 18/10 0.94548
stage 168/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93785  early_stopping: 19/10 0.94548
stage 169/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:32 val_accuracy: 0.93906  early_stopping: 20/10 0.94548
stage 170/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:08 val_accuracy: 0.93794  early_stopping: 21/10 0.94548
stage 171/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:33 val_accuracy: 0.93003  early_stopping: 22/10 0.94548
stage 172/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92888  early_stopping: 23/10 0.94548
stage 173/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.93140  early_stopping: 24/10 0.94548
stage 174/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:28 val_accuracy: 0.93941  early_stopping: 25/10 0.94548
stage 175/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93255  early_stopping: 26/10 0.94548
stage 176/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93190  early_stopping: 27/10 0.94548
stage 177/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93414  early_stopping: 28/10 0.94548
stage 178/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93682  early_stopping: 29/10 0.94548
stage 179/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93069  early_stopping: 30/10 0.94548
stage 180/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93327  early_stopping: 31/10 0.94548
stage 181/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:01 val_accuracy: 0.92605  early_stopping: 32/10 0.94548
stage 182/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93193  early_stopping: 33/10 0.94548
stage 183/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93760  early_stopping: 34/10 0.94548
stage 184/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93265  early_stopping: 35/10 0.94548
stage 185/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.92767  early_stopping: 36/10 0.94548
stage 186/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93747  early_stopping: 37/10 0.94548
stage 187/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:22 val_accuracy: 0.93909  early_stopping: 38/10 0.94548
stage 188/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93695  early_stopping: 39/10 0.94548
stage 189/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.93844  early_stopping: 40/10 0.94548
stage 190/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93053  early_stopping: 41/10 0.94548
stage 191/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:16 val_accuracy: 0.94034  early_stopping: 42/10 0.94548
stage 192/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:58 val_accuracy: 0.93421  early_stopping: 43/10 0.94548
stage 193/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93455  early_stopping: 44/10 0.94548
stage 194/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93383  early_stopping: 45/10 0.94548
stage 195/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93090  early_stopping: 46/10 0.94548
stage 196/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.94563  early_stopping: 0/10 0.94563
stage 197/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.93657  early_stopping: 1/10 0.94563
stage 198/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.94068  early_stopping: 2/10 0.94563
stage 199/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93483  early_stopping: 3/10 0.94563
stage 200/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93050  early_stopping: 4/10 0.94563
stage 201/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93187  early_stopping: 5/10 0.94563
stage 202/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:07:09 val_accuracy: 0.94731  early_stopping: 0/10 0.94731
stage 203/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:28 val_accuracy: 0.93396  early_stopping: 1/10 0.94731
stage 204/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:26 val_accuracy: 0.93642  early_stopping: 2/10 0.94731
stage 205/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:28 val_accuracy: 0.93501  early_stopping: 3/10 0.94731
stage 206/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:27 val_accuracy: 0.93224  early_stopping: 4/10 0.94731
stage 207/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:20 val_accuracy: 0.94221  early_stopping: 5/10 0.94731
stage 208/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93473  early_stopping: 6/10 0.94731
stage 209/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:21 val_accuracy: 0.93726  early_stopping: 7/10 0.94731
stage 210/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:25 val_accuracy: 0.94062  early_stopping: 8/10 0.94731
stage 211/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:23 val_accuracy: 0.93928  early_stopping: 9/10 0.94731
stage 212/โˆž โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 7366/7366 0:00:00 0:06:24 val_accuracy: 0.93931  early_stopping: 10/10 0.94731
Moving best model Juristische_Konsilien_Tuebingen+_202.mlmodel (0.9473143219947815) to Juristische_Konsilien_Tuebingen+_best.mlmodel

real    1387m15.438s
user    19775m16.197s
sys     11007m8.085s

Run training with HTR+ like network specification

(venv3.9) stweil@ocr-01:~/src/gitlab/scripta/escriptorium/Juristische_Konsilien_Tuebingen/Transkribus_Exporte$ time nice ketos train -f page -t list.train -e list.eval -o Juristische_Konsilien_Tuebingen+256 -d cuda:0 --augment --workers 24 -r 0.0001 -B 1 --min-epochs 200 --lag 20 -w 0 -s '[256,64,0,1 Cr4,2,8,4,2 Cr4,2,32,1,1 Mp4,2,4,2 Cr3,3,64,1,1 Mp1,2,1,2 S1(1x0)1,3 Lbx256 Do0.5 Lbx256 Do0.5 Lbx256 Do0.5 Cr255,1,85,1,1]'
WARNING:root:scikit-learn version 1.1.1 is not supported. Minimum required version: 0.17. Maximum required version: 0.19.2. Disabling scikit-learn conversion API.
WARNING:root:Torch version 1.11.0+cu113 has not been tested with coremltools. You may run into unexpected errors. Torch 1.10.2 is the most recent version that has been tested.
[05/27/22 17:05:44] WARNING  alphabet mismatch: chars in training set only: {'ยฝ', '๊Ÿ', 'โ€ก', 'รœ', 'โ€™', 'รป', ']', '[', 'ยบ', 'โ™ƒ', 'X', 'โ•’', '๊ธ', '๊ฏ', '=', 'โ€ '} (not included in accuracy test during        train.py:304
                             training)                                                                                                                                                                                
Trainer already configured with model summary callbacks: [<class 'pytorch_lightning.callbacks.rich_model_summary.RichModelSummary'>]. Skipping setting a default `ModelSummary` callback.
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(val_check_interval=1.0)` was configured so validation will run at the end of the training epoch..
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”“
โ”ƒ    โ”ƒ Name      โ”ƒ Type                     โ”ƒ Params โ”ƒ
โ”กโ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ
โ”‚ 0  โ”‚ net       โ”‚ MultiParamSequential     โ”‚ 15.2 M โ”‚
โ”‚ 1  โ”‚ net.C_0   โ”‚ ActConv2D                โ”‚     72 โ”‚
โ”‚ 2  โ”‚ net.C_1   โ”‚ ActConv2D                โ”‚  2.1 K โ”‚
โ”‚ 3  โ”‚ net.Mp_2  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 4  โ”‚ net.C_3   โ”‚ ActConv2D                โ”‚ 18.5 K โ”‚
โ”‚ 5  โ”‚ net.Mp_4  โ”‚ MaxPool                  โ”‚      0 โ”‚
โ”‚ 6  โ”‚ net.S_5   โ”‚ Reshape                  โ”‚      0 โ”‚
โ”‚ 7  โ”‚ net.L_6   โ”‚ TransposedSummarizingRNN โ”‚  921 K โ”‚
โ”‚ 8  โ”‚ net.Do_7  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 9  โ”‚ net.L_8   โ”‚ TransposedSummarizingRNN โ”‚  1.6 M โ”‚
โ”‚ 10 โ”‚ net.Do_9  โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 11 โ”‚ net.L_10  โ”‚ TransposedSummarizingRNN โ”‚  1.6 M โ”‚
โ”‚ 12 โ”‚ net.Do_11 โ”‚ Dropout                  โ”‚      0 โ”‚
โ”‚ 13 โ”‚ net.C_12  โ”‚ ActConv2D                โ”‚ 11.1 M โ”‚
โ”‚ 14 โ”‚ net.O_13  โ”‚ LinSoftmax               โ”‚ 10.4 K โ”‚
โ””โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
Trainable params: 15.2 M                                                                                                                                                                                              
Non-trainable params: 0                                                                                                                                                                                               
Total params: 15.2 M                                                                                                                                                                                                  
Total estimated model params size (MB): 60                                                                                                                                                                            
stage 0/โˆž  โ”โ”โ”โ”โ”โ•บโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ” 956/7366 0:05:12 0:00:58  early_stopping: 0/20 -inf