Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.5902	0.99	20	1.5519	0.4938	0.3491	0.4938	0.3529
1.4694	1.99	40	1.3730	0.4892	0.4095	0.4892	0.3222
1.3129	2.99	60	1.2052	0.5301	0.3504	0.5301	0.4005
1.1831	3.99	80	1.1142	0.5587	0.4077	0.5587	0.4444
1.0581	4.99	100	0.9930	0.6012	0.5680	0.6012	0.5108
0.9464	5.99	120	0.9263	0.6507	0.6200	0.6507	0.6029
0.8581	6.99	140	0.8400	0.6917	0.6645	0.6917	0.6638
0.7739	7.99	160	0.7829	0.7087	0.6918	0.7087	0.6845
0.6762	8.99	180	0.7512	0.7318	0.7206	0.7318	0.7189
0.6162	9.99	200	0.7409	0.7264	0.7244	0.7264	0.7241
0.5546	10.99	220	0.6936	0.7465	0.7429	0.7465	0.7395
0.4633	11.99	240	0.6779	0.7473	0.7393	0.7473	0.7412
0.4373	12.99	260	0.6736	0.7573	0.7492	0.7573	0.7523
0.4074	13.99	280	0.6534	0.7566	0.7516	0.7566	0.7528
0.39	14.99	300	0.6521	0.7651	0.7603	0.7651	0.7608
0.3766	15.99	320	0.6499	0.7682	0.7607	0.7682	0.7630
0.3507	16.99	340	0.6497	0.7697	0.7686	0.7697	0.7686
0.3589	17.99	360	0.6519	0.7535	0.7485	0.7535	0.7502
0.3261	18.99	380	0.6449	0.7589	0.7597	0.7589	0.7585
0.3234	19.99	400	0.6281	0.7674	0.7651	0.7674	0.7647

jayanta/resnet-152-fv-finetuned-memess

resnet-152-fv-finetuned-memess