Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.599	1.0	40	0.5907	0.8207	0.8515	0.8207	0.8219
0.7842	2.0	80	1.4800	0.6693	0.7271	0.6693	0.6607
0.7716	3.0	120	0.8614	0.7554	0.7853	0.7554	0.7544
0.5976	4.0	160	0.5576	0.8243	0.8470	0.8243	0.8260
0.488	5.0	200	0.4656	0.8555	0.8724	0.8555	0.8546
0.4871	6.0	240	0.4387	0.8672	0.8823	0.8672	0.8672
0.3606	7.0	280	0.3041	0.9045	0.9053	0.9045	0.9034
0.3159	8.0	320	0.3283	0.8976	0.9022	0.8976	0.8961
0.3078	9.0	360	0.2848	0.9125	0.9156	0.9125	0.9124
0.2922	10.0	400	0.2526	0.9180	0.9212	0.9180	0.9184
0.2412	11.0	440	0.2367	0.9281	0.9306	0.9281	0.9280
0.2095	12.0	480	0.2283	0.9314	0.9323	0.9314	0.9305
0.1786	13.0	520	0.1890	0.9408	0.9412	0.9408	0.9408
0.123	14.0	560	0.2071	0.9383	0.9398	0.9383	0.9382
0.1481	15.0	600	0.1854	0.9426	0.9433	0.9426	0.9426
0.125	16.0	640	0.2051	0.9376	0.9400	0.9376	0.9373
0.1135	17.0	680	0.1785	0.9495	0.9496	0.9495	0.9495
0.0815	18.0	720	0.1655	0.9539	0.9542	0.9539	0.9538
0.0784	19.0	760	0.1707	0.9525	0.9527	0.9525	0.9521
0.0905	20.0	800	0.1677	0.9521	0.9523	0.9521	0.9518

momo345/levit-256-finetuned-flower

levit-256-finetuned-flower