Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
78.2197	1.0	1000	77.9227	0.6322	0.4884	2.2807	0.6322	0.6226	0.0652	0.1538
77.1947	2.0	2000	77.1839	0.727	0.3752	2.1097	0.7270	0.7290	0.0480	0.0917
76.83	3.0	3000	76.9771	0.7385	0.3662	2.0823	0.7385	0.7421	0.0612	0.0827
76.6666	4.0	4000	76.7764	0.7725	0.3383	1.9757	0.7725	0.7763	0.0808	0.0697
76.2784	5.0	5000	76.6028	0.773	0.3271	2.0074	0.7730	0.7737	0.0708	0.0668
75.9985	6.0	6000	76.4455	0.7875	0.3138	1.9622	0.7875	0.7874	0.0868	0.0593
75.7339	7.0	7000	76.2669	0.7973	0.3127	1.9661	0.7973	0.7978	0.1123	0.0546
75.504	8.0	8000	76.3972	0.7927	0.3373	2.0115	0.7927	0.7954	0.1318	0.0594
75.3558	9.0	9000	76.4614	0.7785	0.3628	2.1225	0.7785	0.7793	0.1501	0.0648
75.1485	10.0	10000	76.2131	0.795	0.3351	2.0123	0.795	0.7964	0.1467	0.0565
75.1098	11.0	11000	76.3161	0.798	0.3452	2.0430	0.798	0.8002	0.1569	0.0587
74.822	12.0	12000	76.2536	0.788	0.3637	2.0509	0.788	0.7877	0.1659	0.0607
74.8787	13.0	13000	76.2025	0.7965	0.3493	2.0401	0.7965	0.7981	0.1538	0.0582
74.7046	14.0	14000	76.1598	0.8075	0.3335	2.0388	0.8075	0.8058	0.1551	0.0525
74.6157	15.0	15000	76.0894	0.8003	0.3496	1.9931	0.8003	0.8006	0.1615	0.0566
74.6451	16.0	16000	76.0082	0.8065	0.3380	2.0005	0.8065	0.8060	0.1593	0.0530
74.3042	17.0	17000	76.0281	0.8075	0.3398	2.0028	0.8075	0.8097	0.1592	0.0544
74.3261	18.0	18000	75.9836	0.8063	0.3414	2.0447	0.8062	0.8066	0.1614	0.0566
74.1196	19.0	19000	75.8935	0.8103	0.3347	2.0211	0.8103	0.8121	0.1592	0.0595
74.2291	20.0	20000	75.9679	0.815	0.3329	2.0335	0.815	0.8141	0.1586	0.0572
74.268	21.0	21000	76.0052	0.8073	0.3442	2.0847	0.8073	0.8067	0.1633	0.0589
74.0436	22.0	22000	75.9529	0.8093	0.3454	2.1010	0.8093	0.8081	0.1625	0.0547
73.9289	23.0	23000	75.8841	0.8103	0.3420	2.0569	0.8103	0.8104	0.1625	0.0580
73.9519	24.0	24000	75.7295	0.8167	0.3320	2.0459	0.8167	0.8152	0.1575	0.0533
73.9333	25.0	25000	75.6503	0.8165	0.3296	1.9681	0.8165	0.8174	0.1586	0.0523
73.8239	26.0	26000	75.6156	0.8203	0.3245	2.0540	0.8203	0.8192	0.1546	0.0506
73.7011	27.0	27000	75.7075	0.8183	0.3312	2.0996	0.8183	0.8193	0.1594	0.0562
73.4822	28.0	28000	75.5065	0.8247	0.3184	2.0404	0.8247	0.8254	0.1535	0.0548
73.5787	29.0	29000	75.6063	0.8193	0.3295	2.0527	0.8193	0.8192	0.1591	0.0560
73.519	30.0	30000	75.5828	0.8163	0.3351	2.0151	0.8163	0.8173	0.1621	0.0582
73.6516	31.0	31000	75.4986	0.827	0.3147	2.0640	0.827	0.8272	0.1513	0.0539
73.5156	32.0	32000	75.5884	0.8147	0.3355	2.0634	0.8148	0.8137	0.1631	0.0556
73.4564	33.0	33000	75.3992	0.8233	0.3219	2.0498	0.8233	0.8227	0.1536	0.0526
73.3286	34.0	34000	75.4277	0.8197	0.3256	2.0222	0.8197	0.8213	0.1594	0.0540
73.3056	35.0	35000	75.3989	0.8285	0.3136	1.9681	0.8285	0.8303	0.1510	0.0566
73.3272	36.0	36000	75.4398	0.8233	0.3247	2.0504	0.8233	0.8247	0.1583	0.0553
73.2738	37.0	37000	75.3631	0.8207	0.3242	1.9921	0.8207	0.8211	0.1595	0.0546
73.2657	38.0	38000	75.3613	0.8245	0.3231	2.0715	0.8245	0.8232	0.1569	0.0548
73.2045	39.0	39000	75.3697	0.8223	0.3253	2.0207	0.8223	0.8213	0.1571	0.0557
73.1701	40.0	40000	75.3138	0.8277	0.3174	2.0071	0.8277	0.8282	0.1525	0.0557
73.1491	41.0	41000	75.3160	0.827	0.3183	2.0131	0.827	0.8271	0.1549	0.0573
73.1466	42.0	42000	75.3052	0.8297	0.3166	1.9978	0.8297	0.8296	0.1535	0.0558
73.0658	43.0	43000	75.3064	0.8293	0.3166	2.0293	0.8293	0.8292	0.1548	0.0570
73.1394	44.0	44000	75.2527	0.8285	0.3179	2.0172	0.8285	0.8284	0.1540	0.0554
73.2385	45.0	45000	75.2782	0.828	0.3181	2.0026	0.828	0.8280	0.1556	0.0570
73.2207	46.0	46000	75.2624	0.827	0.3194	1.9884	0.827	0.8268	0.1552	0.0559
73.2837	47.0	47000	75.2604	0.8285	0.3195	1.9982	0.8285	0.8283	0.1542	0.0566
73.0848	48.0	48000	75.2454	0.829	0.3188	1.9958	0.8290	0.8288	0.1535	0.0568
73.111	49.0	49000	75.2438	0.8283	0.3196	2.0019	0.8283	0.8282	0.1542	0.0567
73.0278	50.0	50000	75.2362	0.829	0.3192	1.9987	0.8290	0.8288	0.1536	0.0562

Framework versions

Transformers 4.26.1
Pytorch 1.13.1.post200
Datasets 2.9.0
Tokenizers 0.13.2

jordyvl/vit-base-patch16-224-in21k-small_rvl_cdip-NK1000_hint

vit-base-patch16-224-in21k-small_rvl_cdip-NK1000_hint

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jordyvl/
vit-base-patch16-224-in21k-small_rvl_cdip-NK1000_hint