This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.1770
Accuracy: 0.9361

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.687	0.04	10	0.6778	0.6026
0.6605	0.09	20	0.6359	0.7564
0.6074	0.13	30	0.5734	0.7786
0.5464	0.17	40	0.4877	0.8267
0.4606	0.21	50	0.3836	0.8914
0.379	0.26	60	0.3269	0.8877
0.2746	0.3	70	0.2403	0.9198
0.2974	0.34	80	0.2931	0.8890
0.2459	0.39	90	0.2596	0.9016
0.2507	0.43	100	0.2366	0.9123
0.2627	0.47	110	0.2084	0.9224
0.2481	0.51	120	0.2050	0.9270
0.2372	0.56	130	0.2077	0.9267
0.2468	0.6	140	0.2111	0.9230
0.2272	0.64	150	0.1964	0.9267
0.2568	0.68	160	0.1975	0.9270
0.2608	0.73	170	0.2485	0.9048
0.2641	0.77	180	0.2143	0.9227
0.2347	0.81	190	0.1921	0.9307
0.2231	0.86	200	0.1882	0.9315
0.2147	0.9	210	0.1865	0.9329
0.2028	0.94	220	0.1901	0.9294
0.1792	0.98	230	0.1868	0.9297
0.2471	1.03	240	0.2104	0.9190
0.1896	1.07	250	0.1840	0.9321
0.2181	1.11	260	0.1800	0.9318
0.1861	1.16	270	0.1815	0.9305
0.1761	1.2	280	0.1886	0.9299
0.1703	1.24	290	0.1802	0.9315
0.184	1.28	300	0.1845	0.9321
0.1864	1.33	310	0.1791	0.9342
0.1857	1.37	320	0.1760	0.9347
0.1558	1.41	330	0.1798	0.9318
0.1852	1.45	340	0.1810	0.9323
0.183	1.5	350	0.1775	0.9321
0.2055	1.54	360	0.1789	0.9337
0.207	1.58	370	0.2082	0.9208
0.2264	1.63	380	0.1733	0.9339
0.1954	1.67	390	0.1772	0.9337
0.1676	1.71	400	0.1840	0.9302
0.1727	1.75	410	0.1784	0.9305
0.204	1.8	420	0.1731	0.9353
0.1805	1.84	430	0.1805	0.9310
0.1732	1.88	440	0.1773	0.9337
0.1831	1.93	450	0.1768	0.9337
0.1906	1.97	460	0.1967	0.9259
0.1785	2.01	470	0.1765	0.9331
0.1566	2.05	480	0.1749	0.9361
0.1612	2.1	490	0.1718	0.9342
0.1504	2.14	500	0.1770	0.9361
0.1704	2.18	510	0.1721	0.9363
0.1597	2.22	520	0.1711	0.9345
0.1283	2.27	530	0.1775	0.9361
0.1697	2.31	540	0.1722	0.9361
0.1541	2.35	550	0.1729	0.9366
0.1466	2.4	560	0.1708	0.9369
0.1604	2.44	570	0.1720	0.9371
0.1798	2.48	580	0.1718	0.9382
0.134	2.52	590	0.1733	0.9371
0.1215	2.57	600	0.1749	0.9369
0.1284	2.61	610	0.1760	0.9358
0.1449	2.65	620	0.1745	0.9361
0.214	2.7	630	0.1729	0.9382
0.1684	2.74	640	0.1724	0.9369
0.143	2.78	650	0.1737	0.9377
0.1491	2.82	660	0.1753	0.9366
0.1636	2.87	670	0.1743	0.9371
0.1672	2.91	680	0.1724	0.9377
0.1501	2.95	690	0.1720	0.9374

Framework versions

Transformers 4.35.0
Pytorch 1.12.1+cu116
Datasets 2.4.0
Tokenizers 0.14.1

moreover18/vit-base-patch16-224-in21k-finetuned-eurosat

vit-base-patch16-224-in21k-finetuned-eurosat

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

moreover18/
vit-base-patch16-224-in21k-finetuned-eurosat