Training Loss	Epoch	Step	Validation Loss	Accuracy
2.4414	0.9953	105	2.3838	0.102
2.2431	2.0	211	2.2100	0.2698
2.1251	2.9953	316	2.0662	0.4987
2.0119	4.0	422	1.9232	0.6905
1.9134	4.9953	527	1.8080	0.7645
1.8314	6.0	633	1.7059	0.8053
1.7816	6.9953	738	1.6175	0.8215
1.7076	8.0	844	1.5373	0.845
1.6632	8.9953	949	1.4675	0.8592
1.6188	10.0	1055	1.4062	0.863
1.5606	10.9953	1160	1.3510	0.8718
1.5185	12.0	1266	1.3031	0.8718
1.5007	12.9953	1371	1.2591	0.881
1.4573	14.0	1477	1.2201	0.8833
1.4474	14.9953	1582	1.1841	0.8875
1.4308	16.0	1688	1.1524	0.8925
1.4091	16.9953	1793	1.1246	0.8943
1.3683	18.0	1899	1.0986	0.8985
1.365	18.9953	2004	1.0752	0.9042
1.3635	20.0	2110	1.0563	0.9033
1.3422	20.9953	2215	1.0389	0.9043
1.3248	22.0	2321	1.0231	0.9083
1.2961	22.9953	2426	1.0100	0.9093
1.3136	24.0	2532	0.9986	0.9107
1.3067	24.9953	2637	0.9897	0.911
1.2984	26.0	2743	0.9818	0.9115
1.3045	26.9953	2848	0.9759	0.9132
1.291	28.0	2954	0.9717	0.9132
1.2731	28.9953	3059	0.9692	0.9142
1.3034	29.8578	3150	0.9687	0.9137

tangg555/clip-vit-large-patch14-finetuned-clip-vit-large-patch14-mnist_linear_probe

clip-vit-large-patch14-finetuned-clip-vit-large-patch14-mnist_linear_probe