google-vit-base-patch16-384-in21k-batch_16_epoch_4_classes_24_final_withAug_12th_May

This model is a fine-tuned version of Zannatul/final_withAug-google-vit-base-patch16-384-in21k-batch_16_epoch_4_classes_24 on the bengali_food_images dataset. It achieves the following results on the evaluation set:

Model description

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The following hyperparameters were used during training:

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.269	0.09	100	0.4001	0.8723
0.1829	0.17	200	0.0928	0.9728
0.1737	0.26	300	0.1615	0.9457
0.2096	0.34	400	0.4138	0.9022
0.1855	0.43	500	0.1814	0.9511
0.0901	0.51	600	0.1435	0.9579
0.1406	0.6	700	0.1468	0.9620
0.136	0.68	800	0.1532	0.9565
0.0666	0.77	900	0.1177	0.9674
0.1145	0.85	1000	0.1794	0.9497
0.0865	0.94	1100	0.1113	0.9688
0.0612	1.02	1200	0.1270	0.9688
0.0038	1.11	1300	0.0724	0.9783
0.0006	1.19	1400	0.0715	0.9851
0.0007	1.28	1500	0.0616	0.9796
0.0579	1.36	1600	0.1259	0.9715
0.0009	1.45	1700	0.1028	0.9755
0.0295	1.53	1800	0.0637	0.9823
0.0484	1.62	1900	0.0893	0.9783
0.0371	1.71	2000	0.0637	0.9837
0.0359	1.79	2100	0.0389	0.9878
0.0006	1.88	2200	0.0750	0.9823
0.0189	1.96	2300	0.0451	0.9851
0.0442	2.05	2400	0.0772	0.9796
0.0006	2.13	2500	0.1988	0.9620
0.006	2.22	2600	0.0659	0.9864
0.0093	2.3	2700	0.0754	0.9810
0.0008	2.39	2800	0.0800	0.9783
0.0003	2.47	2900	0.0617	0.9864
0.0094	2.56	3000	0.0736	0.9837
0.0001	2.64	3100	0.0538	0.9823
0.001	2.73	3200	0.0606	0.9878
0.0001	2.81	3300	0.0433	0.9864
0.0001	2.9	3400	0.0583	0.9823
0.0001	2.98	3500	0.0388	0.9905
0.0001	3.07	3600	0.0408	0.9891
0.0001	3.15	3700	0.0375	0.9891
0.0001	3.24	3800	0.0367	0.9878
0.0001	3.32	3900	0.0355	0.9878
0.0001	3.41	4000	0.0395	0.9878
0.0001	3.5	4100	0.0382	0.9878
0.0001	3.58	4200	0.0399	0.9891
0.0001	3.67	4300	0.0396	0.9891
0.0072	3.75	4400	0.0355	0.9905
0.0001	3.84	4500	0.0284	0.9918
0.0001	3.92	4600	0.0282	0.9905