Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.3683	0.99	20	1.1798	0.5703	0.4914	0.5703	0.4915
1.0113	1.99	40	1.0384	0.6159	0.6813	0.6159	0.6274
0.7581	2.99	60	0.8348	0.6808	0.7377	0.6808	0.6840
0.6241	3.99	80	0.6034	0.7713	0.7864	0.7713	0.7735
0.4999	4.99	100	0.5481	0.7944	0.8000	0.7944	0.7909
0.3981	5.99	120	0.5253	0.8022	0.8091	0.8022	0.8000
0.3484	6.99	140	0.4688	0.8238	0.8147	0.8238	0.8146
0.3142	7.99	160	0.6245	0.7867	0.8209	0.7867	0.7920
0.2339	8.99	180	0.5053	0.8362	0.8426	0.8362	0.8355
0.2284	9.99	200	0.5070	0.8230	0.8220	0.8230	0.8187
0.1824	10.99	220	0.5780	0.8006	0.8138	0.8006	0.8035
0.1561	11.99	240	0.5429	0.8253	0.8197	0.8253	0.8218
0.1229	12.99	260	0.5325	0.8331	0.8296	0.8331	0.8303
0.1232	13.99	280	0.5595	0.8277	0.8290	0.8277	0.8273
0.118	14.99	300	0.5974	0.8292	0.8345	0.8292	0.8299
0.11	15.99	320	0.5796	0.8253	0.8228	0.8253	0.8231
0.0948	16.99	340	0.5581	0.8346	0.8358	0.8346	0.8349
0.0985	17.99	360	0.5700	0.8338	0.8301	0.8338	0.8318
0.0821	18.99	380	0.5756	0.8331	0.8343	0.8331	0.8335
0.0813	19.99	400	0.5984	0.8323	0.8312	0.8323	0.8315

jayanta/mit-b2-fv-finetuned-memes

mit-b2-fv-finetuned-memes