ViT-Face-Recognition/saved_results/Models/MobileNet_V2/training_log.csv

1	epoch	accuracy	loss	top-10-accuracy	top-100-accuracy	top-5-accuracy	val_accuracy	val_loss	val_top-10-accuracy	val_top-100-accuracy	val_top-5-accuracy
2	0	0.4907640516757965	2.949651002883911	0.7118712067604065	0.868656575679779	0.6537961363792419	0.7002686262130737	1.4239873886108398	0.8952919840812683	0.9758934378623962	0.853851854801178
3	1	0.795885443687439	0.9672361016273499	0.9348357319831848	0.9854981303215027	0.9071050882339478	0.8360090255737305	0.7465912103652954	0.9546831846237183	0.9909353852272034	0.9332756400108337
4	2	0.8633260726928711	0.6249759793281555	0.9625975489616394	0.9924740791320801	0.9446355700492859	0.8706443309783936	0.5745269060134888	0.9690758585929871	0.994270920753479	0.952448844909668
5	3	0.8968734741210938	0.46120527386665344	0.9748672246932983	0.9952197670936584	0.9618654847145081	0.902364194393158	0.4231894016265869	0.9791271686553955	0.9963652491569519	0.9674080610275269
6	4	0.9181494116783142	0.36037951707839966	0.9819280505180359	0.9967595338821411	0.9721487760543823	0.9246756434440613	0.32385993003845215	0.985480010509491	0.9976319670677185	0.9770964980125427
7	5	0.9327888488769531	0.2913418114185333	0.9865629076957703	0.997750461101532	0.9787262082099915	0.9336193799972534	0.27826163172721863	0.9886055588722229	0.998383104801178	0.9815842509269714
8	6	0.9436453580856323	0.24069969356060028	0.989750325679779	0.9983972907066345	0.9834964871406555	0.931856095790863	0.2713744342327118	0.9899677634239197	0.9985804557800293	0.9825645685195923
9	7	0.9521682262420654	0.20128048956394196	0.9921678304672241	0.9988619685173035	0.9871729612350464	0.9549760222434998	0.18512850999832153	0.9941691160202026	0.9992679357528687	0.9899550676345825
10	8	0.9589677453041077	0.1698894202709198	0.9939650893211365	0.9991915822029114	0.9898921251296997	0.9510611295700073	0.1860179901123047	0.9949711561203003	0.9993952512741089	0.9903051853179932
11	9	0.9644138216972351	0.14514721930027008	0.9954121708869934	0.9994133114814758	0.9920172095298767	0.9577195644378662	0.1596861630678177	0.9961488246917725	0.9995161890983582	0.9926095008850098
12	10	0.9689231514930725	0.12475422024726868	0.9964313507080078	0.9996000528335571	0.993649959564209	0.955058753490448	0.1626364290714264	0.9965816736221313	0.9996753334999084	0.9926795363426208
13	11	0.9727602005004883	0.10766829550266266	0.9972864985466003	0.9997209906578064	0.9950493574142456	0.9752568602561951	0.09437419474124908	0.9983195066452026	0.9998599290847778	0.9966517090797424
14	12	0.9758743643760681	0.09373524785041809	0.9979273080825806	0.9997881650924683	0.996117353439331	0.973614513874054	0.09697101265192032	0.9983958601951599	0.9999045133590698	0.9966962337493896
15	13	0.9786292910575867	0.08203183114528656	0.9984199404716492	0.9998556971549988	0.996892511844635	0.9755051136016846	0.08805303275585175	0.9986759424209595	0.9999172687530518	0.9971482157707214
16	14	0.9808819890022278	0.07264290004968643	0.9987650513648987	0.9999023675918579	0.9975138902664185	0.976300835609436	0.08455612510442734	0.998968780040741	0.9999745488166809	0.9976256489753723
17	15	0.9828794002532959	0.06430379301309586	0.9990943074226379	0.9999317526817322	0.9980705380439758	0.977268397808075	0.07780636847019196	0.9992552399635315	0.9999809265136719	0.9981221556663513
18	16	0.9845736622810364	0.05719618871808052	0.9993026256561279	0.9999561309814453	0.998477578163147	0.9778603911399841	0.0757608637213707	0.9992616176605225	0.9999809265136719	0.9982303380966187
19	17	0.9859727025032043	0.051699280738830566	0.9994804859161377	0.9999752640724182	0.9987739324569702	0.9787770509719849	0.07049136608839035	0.9994907379150391	0.9999809265136719	0.9985740780830383
20	18	0.9872153997421265	0.04683251306414604	0.9995855093002319	0.9999851584434509	0.9990158081054688	0.9792226552963257	0.06943082809448242	0.9995735287666321	0.9999872446060181	0.9986441135406494
21	19	0.9882335662841797	0.04242420196533203	0.9997022151947021	0.9999908208847046	0.9992198348045349	0.9825581908226013	0.05836047977209091	0.9996626377105713	0.999993622303009	0.9990515112876892
22	20	0.9891318082809448	0.039091356098651886	0.9997729659080505	0.9999939799308777	0.9993308782577515	0.9775166511535645	0.07386624068021774	0.9995289444923401	0.999993622303009	0.9984404444694519
23	21	0.9898553490638733	0.03624232858419418	0.999825656414032	0.9999982118606567	0.999459981918335	0.9878098368644714	0.04147448390722275	0.9998790621757507	1.0	0.99949711561203
24	22	0.9906644821166992	0.03344619646668434	0.999862790107727	0.9999974966049194	0.9995526671409607	0.9770392179489136	0.07516016811132431	0.9995862245559692	1.0	0.9984785914421082
25	23	0.991235613822937	0.03114890120923519	0.9999006390571594	0.9999978542327881	0.9996435046195984	0.9889938235282898	0.03747271001338959	0.9998981356620789	1.0	0.9996180534362793
26	24	0.9917523264884949	0.029260696843266487	0.9999108910560608	0.9999985694885254	0.9996809959411621	0.9876443147659302	0.041266944259405136	0.9999045133590698	1.0	0.9995671510696411