This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3360

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 4.6875e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 800.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.0249	1.0	3583	1.0248
0.9939	2.0	7166	0.9940
0.9684	3.0	10749	0.9675
0.9411	4.0	14332	0.9439
0.9385	5.0	17915	0.9319
0.9105	6.0	21498	0.9036
0.87	7.0	25081	0.8847
0.8103	8.0	28664	0.8216
0.7776	9.0	32247	0.7917
0.772	10.0	35830	0.7733
0.7577	11.0	39413	0.7593
0.7543	12.0	42996	0.7469
0.7229	13.0	46579	0.7348
0.7367	14.0	50162	0.7231
0.7186	15.0	53745	0.7162
0.6945	16.0	57328	0.7074
0.6966	17.0	60911	0.6957
0.6699	18.0	64494	0.6815
0.6837	19.0	68077	0.6737
0.6981	20.0	71660	0.6679
0.6692	21.0	75243	0.6618
0.6542	22.0	78826	0.6589
0.6692	23.0	82409	0.6433
0.6409	24.0	85992	0.6381
0.6417	25.0	89575	0.6330
0.6184	26.0	93158	0.6277
0.6111	27.0	96741	0.6211
0.6445	28.0	100324	0.6169
0.6258	29.0	103907	0.6131
0.592	30.0	107490	0.6102
0.6087	31.0	111073	0.6094
0.5809	32.0	114656	0.6021
0.5883	33.0	118239	0.6023
0.5945	34.0	121822	0.5976
0.6055	35.0	125405	0.5937
0.5927	36.0	128988	0.5887
0.5596	37.0	132571	0.5854
0.5683	38.0	136154	0.5787
0.5639	39.0	139737	0.5796
0.5931	40.0	143320	0.5734
0.5773	41.0	146903	0.5735
0.5794	42.0	150486	0.5701
0.558	43.0	154069	0.5638
0.5822	44.0	157652	0.5647
0.5671	45.0	161235	0.5636
0.5509	46.0	164818	0.5666
0.563	47.0	168401	0.5586
0.5645	48.0	171984	0.5540
0.5417	49.0	175567	0.5491
0.5516	50.0	179150	0.5476
0.5508	51.0	182733	0.5487
0.5309	52.0	186316	0.5420
0.5274	53.0	189899	0.5401
0.5349	54.0	193482	0.5388
0.5375	55.0	197065	0.5346
0.5127	56.0	200648	0.5337
0.5168	57.0	204231	0.5338
0.5292	58.0	207814	0.5336
0.526	59.0	211397	0.5278
0.5335	60.0	214980	0.5240
0.5343	61.0	218563	0.5236
0.5121	62.0	222146	0.5226
0.5293	63.0	225729	0.5189
0.5213	64.0	229312	0.5178
0.5063	65.0	232895	0.5164
0.5037	66.0	236478	0.5134
0.5159	67.0	240061	0.5096
0.5112	68.0	243644	0.5094
0.5056	69.0	247227	0.5046
0.5004	70.0	250810	0.5031
0.5031	71.0	254393	0.5024
0.4931	72.0	257976	0.4983
0.4887	73.0	261559	0.4947
0.4888	74.0	265142	0.4901
0.4668	75.0	268725	0.4890
0.4804	76.0	272308	0.4833
0.4662	77.0	275891	0.4789
0.4969	78.0	279474	0.4745
0.4552	79.0	283057	0.4715
0.4553	80.0	286640	0.4707
0.4716	81.0	290223	0.4681
0.4643	82.0	293806	0.4631
0.4596	83.0	297389	0.4621
0.4429	84.0	300972	0.4611
0.4684	85.0	304555	0.4600
0.4461	86.0	308138	0.4553
0.451	87.0	311721	0.4513
0.4675	88.0	315304	0.4512
0.4433	89.0	318887	0.4488
0.4708	90.0	322470	0.4450
0.4523	91.0	326053	0.4434
0.4395	92.0	329636	0.4421
0.4746	93.0	333219	0.4427
0.4537	94.0	336802	0.4382
0.4287	95.0	340385	0.4366
0.4256	96.0	343968	0.4365
0.4406	97.0	347551	0.4374
0.4301	98.0	351134	0.4327
0.4107	99.0	354717	0.4338
0.4337	100.0	358300	0.4310
0.4117	101.0	361883	0.4291
0.4468	102.0	365466	0.4289
0.4124	103.0	369049	0.4263
0.4338	104.0	372632	0.4240
0.4232	105.0	376215	0.4246
0.4122	106.0	379798	0.4225
0.4242	107.0	383381	0.4228
0.4372	108.0	386964	0.4206
0.4163	109.0	390547	0.4199
0.4347	110.0	394130	0.4208
0.4046	111.0	397713	0.4181
0.444	112.0	401296	0.4176
0.4057	113.0	404879	0.4155
0.4195	114.0	408462	0.4159
0.4376	115.0	412045	0.4172
0.4197	116.0	415628	0.4142
0.4286	117.0	419211	0.4116
0.4222	118.0	422794	0.4108
0.4152	119.0	426377	0.4137
0.4141	120.0	429960	0.4105
0.419	121.0	433543	0.4089
0.4205	122.0	437126	0.4083
0.4187	123.0	440709	0.4087
0.39	124.0	444292	0.4065
0.3914	125.0	447875	0.4069
0.4077	126.0	451458	0.4068
0.3943	127.0	455041	0.4045
0.4167	128.0	458624	0.4049
0.4162	129.0	462207	0.4044
0.3902	130.0	465790	0.4036
0.398	131.0	469373	0.4014
0.4082	132.0	472956	0.3995
0.4093	133.0	476539	0.4011
0.3893	134.0	480122	0.4016
0.3961	135.0	483705	0.4000
0.4075	136.0	487288	0.3982
0.4007	137.0	490871	0.4006
0.3961	138.0	494454	0.3973
0.3775	139.0	498037	0.3981
0.4088	140.0	501620	0.3977
0.4036	141.0	505203	0.3962
0.4006	142.0	508786	0.3950
0.3895	143.0	512369	0.3964
0.3908	144.0	515952	0.3959
0.3933	145.0	519535	0.3959
0.4021	146.0	523118	0.3946
0.3912	147.0	526701	0.3944
0.3881	148.0	530284	0.3925
0.3984	149.0	533867	0.3923
0.3925	150.0	537450	0.3920
0.3896	151.0	541033	0.3906
0.3925	152.0	544616	0.3916
0.3975	153.0	548199	0.3911
0.3925	154.0	551782	0.3909
0.3857	155.0	555365	0.3891
0.4006	156.0	558948	0.3900
0.3961	157.0	562531	0.3890
0.389	158.0	566114	0.3903
0.3923	159.0	569697	0.3874
0.4047	160.0	573280	0.3873
0.3928	161.0	576863	0.3868
0.3862	162.0	580446	0.3860
0.3931	163.0	584029	0.3870
0.3882	164.0	587612	0.3872
0.3806	165.0	591195	0.3870
0.372	166.0	594778	0.3845
0.3758	167.0	598361	0.3852
0.3904	168.0	601944	0.3833
0.3891	169.0	605527	0.3843
0.3965	170.0	609110	0.3835
0.3798	171.0	612693	0.3828
0.3864	172.0	616276	0.3824
0.3909	173.0	619859	0.3841
0.3771	174.0	623442	0.3826
0.376	175.0	627025	0.3820
0.3747	176.0	630608	0.3825
0.3785	177.0	634191	0.3815
0.3871	178.0	637774	0.3795
0.3829	179.0	641357	0.3819
0.3862	180.0	644940	0.3817
0.3798	181.0	648523	0.3817
0.3867	182.0	652106	0.3814
0.3661	183.0	655689	0.3795
0.3707	184.0	659272	0.3798
0.3798	185.0	662855	0.3797
0.3891	186.0	666438	0.3797
0.3577	187.0	670021	0.3783
0.3846	188.0	673604	0.3782
0.3647	189.0	677187	0.3771
0.384	190.0	680770	0.3773
0.3717	191.0	684353	0.3778
0.3756	192.0	687936	0.3763
0.4	193.0	691519	0.3768
0.3507	194.0	695102	0.3746
0.3719	195.0	698685	0.3755
0.3659	196.0	702268	0.3753
0.3753	197.0	705851	0.3753
0.372	198.0	709434	0.3744
0.3973	199.0	713017	0.3749
0.3622	200.0	716600	0.3743
0.3691	201.0	720183	0.3727
0.3741	202.0	723766	0.3743
0.3893	203.0	727349	0.3733
0.3743	204.0	730932	0.3727
0.3701	205.0	734515	0.3721
0.3743	206.0	738098	0.3736
0.3746	207.0	741681	0.3723
0.3985	208.0	745264	0.3738
0.3583	209.0	748847	0.3715
0.3706	210.0	752430	0.3714
0.3782	211.0	756013	0.3723
0.3724	212.0	759596	0.3721
0.3716	213.0	763179	0.3710
0.3764	214.0	766762	0.3722
0.3698	215.0	770345	0.3714
0.3613	216.0	773928	0.3695
0.3646	217.0	777511	0.3699
0.364	218.0	781094	0.3687
0.366	219.0	784677	0.3704
0.3603	220.0	788260	0.3702
0.3565	221.0	791843	0.3691
0.3784	222.0	795426	0.3692
0.3761	223.0	799009	0.3685
0.3607	224.0	802592	0.3674
0.3471	225.0	806175	0.3682
0.3669	226.0	809758	0.3687
0.3864	227.0	813341	0.3695
0.3667	228.0	816924	0.3682
0.3782	229.0	820507	0.3667
0.3841	230.0	824090	0.3664
0.3698	231.0	827673	0.3660
0.3579	232.0	831256	0.3661
0.3754	233.0	834839	0.3679
0.3711	234.0	838422	0.3672
0.3768	235.0	842005	0.3672
0.3796	236.0	845588	0.3656
0.3701	237.0	849171	0.3658
0.3691	238.0	852754	0.3675
0.3695	239.0	856337	0.3651
0.3703	240.0	859920	0.3646
0.3562	241.0	863503	0.3635
0.3663	242.0	867086	0.3646
0.3667	243.0	870669	0.3655
0.3753	244.0	874252	0.3662
0.372	245.0	877835	0.3648
0.3764	246.0	881418	0.3654
0.381	247.0	885001	0.3643
0.3695	248.0	888584	0.3639
0.3463	249.0	892167	0.3657
0.3567	250.0	895750	0.3638
0.3733	251.0	899333	0.3621
0.3547	252.0	902916	0.3627
0.3428	253.0	906499	0.3642
0.3646	254.0	910082	0.3628
0.361	255.0	913665	0.3629
0.3672	256.0	917248	0.3624
0.3573	257.0	920831	0.3608
0.3662	258.0	924414	0.3623
0.3729	259.0	927997	0.3640
0.3601	260.0	931580	0.3630
0.3754	261.0	935163	0.3617
0.3672	262.0	938746	0.3625
0.3384	263.0	942329	0.3618
0.3625	264.0	945912	0.3619
0.3662	265.0	949495	0.3614
0.3492	266.0	953078	0.3621
0.3586	267.0	956661	0.3608
0.3636	268.0	960244	0.3619
0.3435	269.0	963827	0.3606
0.3787	270.0	967410	0.3614
0.353	271.0	970993	0.3613
0.3549	272.0	974576	0.3610
0.3638	273.0	978159	0.3612
0.3732	274.0	981742	0.3610
0.376	275.0	985325	0.3603
0.3719	276.0	988908	0.3608
0.3405	277.0	992491	0.3579
0.3693	278.0	996074	0.3597
0.3657	279.0	999657	0.3580
0.3693	280.0	1003240	0.3596
0.3684	281.0	1006823	0.3600
0.3725	282.0	1010406	0.3595
0.3565	283.0	1013989	0.3596
0.3648	284.0	1017572	0.3579
0.3598	285.0	1021155	0.3581
0.3486	286.0	1024738	0.3593
0.3453	287.0	1028321	0.3588
0.3479	288.0	1031904	0.3599
0.3547	289.0	1035487	0.3572
0.3628	290.0	1039070	0.3575
0.3545	291.0	1042653	0.3583
0.3391	292.0	1046236	0.3588
0.354	293.0	1049819	0.3575
0.3559	294.0	1053402	0.3579
0.3547	295.0	1056985	0.3576
0.3509	296.0	1060568	0.3576
0.3564	297.0	1064151	0.3583
0.3504	298.0	1067734	0.3577
0.3582	299.0	1071317	0.3577
0.3511	300.0	1074900	0.3565
0.3532	301.0	1078483	0.3575
0.3612	302.0	1082066	0.3571
0.3653	303.0	1085649	0.3558
0.3623	304.0	1089232	0.3565
0.3515	305.0	1092815	0.3565
0.3513	306.0	1096398	0.3565
0.3584	307.0	1099981	0.3569
0.3454	308.0	1103564	0.3551
0.3565	309.0	1107147	0.3559
0.3845	310.0	1110730	0.3541
0.358	311.0	1114313	0.3551
0.3388	312.0	1117896	0.3556
0.3494	313.0	1121479	0.3551
0.3643	314.0	1125062	0.3556
0.3457	315.0	1128645	0.3562
0.3603	316.0	1132228	0.3552
0.3628	317.0	1135811	0.3544
0.3491	318.0	1139394	0.3540
0.3482	319.0	1142977	0.3540
0.3506	320.0	1146560	0.3553
0.3559	321.0	1150143	0.3559
0.3478	322.0	1153726	0.3549
0.3469	323.0	1157309	0.3538
0.3482	324.0	1160892	0.3555
0.3605	325.0	1164475	0.3534
0.3545	326.0	1168058	0.3544
0.3521	327.0	1171641	0.3554
0.3577	328.0	1175224	0.3534
0.3429	329.0	1178807	0.3526
0.3522	330.0	1182390	0.3534
0.3632	331.0	1185973	0.3557
0.349	332.0	1189556	0.3526
0.3601	333.0	1193139	0.3530
0.3548	334.0	1196722	0.3536
0.3554	335.0	1200305	0.3515
0.3589	336.0	1203888	0.3533
0.3624	337.0	1207471	0.3512
0.3312	338.0	1211054	0.3520
0.3441	339.0	1214637	0.3516
0.3558	340.0	1218220	0.3534
0.3553	341.0	1221803	0.3519
0.3367	342.0	1225386	0.3525
0.3516	343.0	1228969	0.3533
0.3627	344.0	1232552	0.3548
0.3501	345.0	1236135	0.3515
0.3609	346.0	1239718	0.3518
0.3424	347.0	1243301	0.3520
0.3463	348.0	1246884	0.3520
0.3358	349.0	1250467	0.3529
0.3457	350.0	1254050	0.3521
0.3479	351.0	1257633	0.3506
0.3352	352.0	1261216	0.3516
0.3592	353.0	1264799	0.3517
0.363	354.0	1268382	0.3510
0.3553	355.0	1271965	0.3511
0.3553	356.0	1275548	0.3507
0.3413	357.0	1279131	0.3510
0.3707	358.0	1282714	0.3500
0.3654	359.0	1286297	0.3506
0.336	360.0	1289880	0.3496
0.3354	361.0	1293463	0.3514
0.3598	362.0	1297046	0.3505
0.3632	363.0	1300629	0.3487
0.3464	364.0	1304212	0.3508
0.3375	365.0	1307795	0.3508
0.3568	366.0	1311378	0.3506
0.3419	367.0	1314961	0.3514
0.3385	368.0	1318544	0.3487
0.3503	369.0	1322127	0.3499
0.3643	370.0	1325710	0.3504
0.3379	371.0	1329293	0.3481
0.3577	372.0	1332876	0.3488
0.3506	373.0	1336459	0.3501
0.3429	374.0	1340042	0.3481
0.3633	375.0	1343625	0.3483
0.3505	376.0	1347208	0.3490
0.3399	377.0	1350791	0.3487
0.3547	378.0	1354374	0.3485
0.3553	379.0	1357957	0.3486
0.3727	380.0	1361540	0.3484
0.3466	381.0	1365123	0.3488
0.363	382.0	1368706	0.3490
0.3565	383.0	1372289	0.3487
0.3457	384.0	1375872	0.3486
0.3375	385.0	1379455	0.3483
0.3677	386.0	1383038	0.3493
0.3623	387.0	1386621	0.3484
0.3345	388.0	1390204	0.3500
0.3564	389.0	1393787	0.3475
0.3608	390.0	1397370	0.3488
0.3335	391.0	1400953	0.3487
0.3453	392.0	1404536	0.3490
0.3528	393.0	1408119	0.3463
0.3534	394.0	1411702	0.3484
0.3304	395.0	1415285	0.3478
0.346	396.0	1418868	0.3481
0.3547	397.0	1422451	0.3481
0.3446	398.0	1426034	0.3490
0.352	399.0	1429617	0.3485
0.3537	400.0	1433200	0.3478
0.3629	401.0	1436783	0.3456
0.3307	402.0	1440366	0.3476
0.3532	403.0	1443949	0.3470
0.3372	404.0	1447532	0.3475
0.3483	405.0	1451115	0.3476
0.3352	406.0	1454698	0.3482
0.3611	407.0	1458281	0.3475
0.3593	408.0	1461864	0.3473
0.3603	409.0	1465447	0.3470
0.3393	410.0	1469030	0.3462
0.3497	411.0	1472613	0.3461
0.3598	412.0	1476196	0.3465
0.3462	413.0	1479779	0.3465
0.3351	414.0	1483362	0.3466
0.3534	415.0	1486945	0.3453
0.3355	416.0	1490528	0.3480
0.3475	417.0	1494111	0.3463
0.36	418.0	1497694	0.3471
0.3579	419.0	1501277	0.3473
0.3323	420.0	1504860	0.3466
0.3681	421.0	1508443	0.3459
0.3531	422.0	1512026	0.3464
0.3529	423.0	1515609	0.3465
0.3441	424.0	1519192	0.3450
0.3355	425.0	1522775	0.3459
0.346	426.0	1526358	0.3461
0.3589	427.0	1529941	0.3466
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0.3475	430.0	1540690	0.3459
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0.338	769.0	2755327	0.3367
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0.36	775.0	2776825	0.3378
0.3393	776.0	2780408	0.3385
0.335	777.0	2783991	0.3351
0.3306	778.0	2787574	0.3359
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0.3401	780.0	2794740	0.3375
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0.3357	786.0	2816238	0.3381
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0.3349	788.0	2823404	0.3378
0.343	789.0	2826987	0.3381
0.3418	790.0	2830570	0.3371
0.3355	791.0	2834153	0.3375
0.3189	792.0	2837736	0.3375
0.3533	793.0	2841319	0.3380
0.346	794.0	2844902	0.3382
0.3304	795.0	2848485	0.3371
0.3438	796.0	2852068	0.3375
0.3563	797.0	2855651	0.3375
0.3523	798.0	2859234	0.3371
0.3234	799.0	2862817	0.3364
0.34	800.0	2866400	0.3378

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

jaypratap/vit-pretraining-2024_03_14

vit-pretraining-2024_03_14

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jaypratap/
vit-pretraining-2024_03_14