Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7496	1.0	210	1.7976	0.3472
1.5383	2.0	420	1.6204	0.4208
1.4952	3.0	630	1.5130	0.4792
1.4119	4.0	840	1.4325	0.5069
1.2574	5.0	1050	1.3759	0.5083
1.22	6.0	1260	1.3241	0.5389
1.2337	7.0	1470	1.2850	0.5361
1.0326	8.0	1680	1.2407	0.5486
1.0324	9.0	1890	1.2108	0.5639
1.0585	10.0	2100	1.1799	0.5708
1.0901	11.0	2310	1.1469	0.5847
0.9732	12.0	2520	1.1306	0.5972
0.9296	13.0	2730	1.1049	0.6111
0.9816	14.0	2940	1.0782	0.6208
1.0264	15.0	3150	1.0600	0.6264
0.8306	16.0	3360	1.0358	0.6375
0.8897	17.0	3570	1.0187	0.6375
0.8084	18.0	3780	1.0035	0.6472
0.8002	19.0	3990	0.9875	0.65
0.8268	20.0	4200	0.9731	0.6556
0.7675	21.0	4410	0.9556	0.6667
0.771	22.0	4620	0.9414	0.6861
0.8116	23.0	4830	0.9237	0.6917
0.7656	24.0	5040	0.9177	0.675
0.7831	25.0	5250	0.9004	0.6903
0.6994	26.0	5460	0.8887	0.7014
0.7049	27.0	5670	0.8797	0.7014
0.7354	28.0	5880	0.8668	0.7125
0.6543	29.0	6090	0.8590	0.7083
0.6978	30.0	6300	0.8484	0.7125
0.6395	31.0	6510	0.8381	0.7125
0.7869	32.0	6720	0.8304	0.7194
0.5881	33.0	6930	0.8210	0.7236
0.6378	34.0	7140	0.8087	0.7333
0.6627	35.0	7350	0.8033	0.7333
0.5914	36.0	7560	0.7930	0.7278
0.6632	37.0	7770	0.7855	0.7292
0.7052	38.0	7980	0.7778	0.7375
0.587	39.0	8190	0.7701	0.7319
0.6044	40.0	8400	0.7633	0.7444
0.5306	41.0	8610	0.7567	0.7472
0.5634	42.0	8820	0.7522	0.7472
0.588	43.0	9030	0.7456	0.7528
0.5567	44.0	9240	0.7401	0.7597
0.5823	45.0	9450	0.7283	0.7583
0.5638	46.0	9660	0.7241	0.7653
0.5413	47.0	9870	0.7210	0.7583
0.4506	48.0	10080	0.7134	0.7653
0.5293	49.0	10290	0.7083	0.7667
0.454	50.0	10500	0.7047	0.7708
0.5858	51.0	10710	0.6984	0.7778
0.5209	52.0	10920	0.6933	0.7736
0.5704	53.0	11130	0.6865	0.7792
0.4993	54.0	11340	0.6835	0.7778
0.4677	55.0	11550	0.6787	0.7847
0.4808	56.0	11760	0.6746	0.7792
0.4543	57.0	11970	0.6716	0.7889
0.4304	58.0	12180	0.6645	0.7792
0.474	59.0	12390	0.6591	0.7917
0.444	60.0	12600	0.6544	0.7875
0.4743	61.0	12810	0.6526	0.7903
0.5157	62.0	13020	0.6480	0.7972
0.5253	63.0	13230	0.6451	0.7903
0.4351	64.0	13440	0.6419	0.7958
0.3988	65.0	13650	0.6382	0.7944
0.4919	66.0	13860	0.6319	0.8028
0.4085	67.0	14070	0.6314	0.8042
0.4562	68.0	14280	0.6274	0.8056
0.4822	69.0	14490	0.6223	0.8028
0.4173	70.0	14700	0.6213	0.8042
0.4775	71.0	14910	0.6181	0.8097
0.4069	72.0	15120	0.6156	0.8083
0.429	73.0	15330	0.6112	0.8097
0.4165	74.0	15540	0.6093	0.8111
0.452	75.0	15750	0.6048	0.8139
0.3727	76.0	15960	0.6018	0.8139
0.4692	77.0	16170	0.6004	0.8208
0.4075	78.0	16380	0.5964	0.8153
0.4102	79.0	16590	0.5944	0.8167
0.4043	80.0	16800	0.5937	0.8208
0.3813	81.0	17010	0.5891	0.8167
0.4022	82.0	17220	0.5859	0.8194
0.423	83.0	17430	0.5846	0.8222
0.4503	84.0	17640	0.5831	0.8222
0.3758	85.0	17850	0.5812	0.8292
0.3469	86.0	18060	0.5796	0.8222
0.3344	87.0	18270	0.5764	0.8236
0.3928	88.0	18480	0.5742	0.8333
0.4039	89.0	18690	0.5726	0.8292
0.3589	90.0	18900	0.5702	0.8333
0.3135	91.0	19110	0.5676	0.8306
0.3588	92.0	19320	0.5663	0.8306
0.3851	93.0	19530	0.5641	0.8333
0.3564	94.0	19740	0.5629	0.8292
0.3446	95.0	19950	0.5598	0.8375
0.3586	96.0	20160	0.5583	0.8361
0.3362	97.0	20370	0.5560	0.8347
0.4145	98.0	20580	0.5564	0.8361
0.3158	99.0	20790	0.5539	0.8361
0.4119	100.0	21000	0.5520	0.8375
0.3541	101.0	21210	0.5507	0.8389
0.4606	102.0	21420	0.5491	0.8389
0.3529	103.0	21630	0.5479	0.8375
0.3412	104.0	21840	0.5467	0.8347
0.3532	105.0	22050	0.5460	0.8361
0.39	106.0	22260	0.5439	0.8375
0.3764	107.0	22470	0.5429	0.8389
0.3163	108.0	22680	0.5411	0.8389
0.3287	109.0	22890	0.5398	0.8389
0.3249	110.0	23100	0.5384	0.8403
0.3339	111.0	23310	0.5372	0.8389
0.3073	112.0	23520	0.5361	0.8417
0.336	113.0	23730	0.5352	0.8389
0.2888	114.0	23940	0.5335	0.8403
0.2934	115.0	24150	0.5326	0.8431
0.3194	116.0	24360	0.5325	0.8403
0.3247	117.0	24570	0.5312	0.8403
0.3566	118.0	24780	0.5302	0.8403
0.3298	119.0	24990	0.5292	0.8417
0.3224	120.0	25200	0.5280	0.8431
0.3841	121.0	25410	0.5277	0.8403
0.2983	122.0	25620	0.5273	0.8417
0.3206	123.0	25830	0.5268	0.8417
0.3243	124.0	26040	0.5262	0.8403
0.3075	125.0	26250	0.5249	0.8403
0.3079	126.0	26460	0.5238	0.8417
0.3295	127.0	26670	0.5235	0.8431
0.3084	128.0	26880	0.5233	0.8403
0.3289	129.0	27090	0.5224	0.8444
0.3489	130.0	27300	0.5225	0.8417
0.3362	131.0	27510	0.5215	0.8444
0.3187	132.0	27720	0.5209	0.8431
0.332	133.0	27930	0.5204	0.8431
0.3196	134.0	28140	0.5201	0.8417
0.2873	135.0	28350	0.5194	0.8431
0.3391	136.0	28560	0.5191	0.8444
0.3006	137.0	28770	0.5184	0.8444
0.2912	138.0	28980	0.5182	0.8431
0.3421	139.0	29190	0.5180	0.8431
0.2924	140.0	29400	0.5178	0.8417
0.2989	141.0	29610	0.5177	0.8431
0.3207	142.0	29820	0.5175	0.8431
0.3041	143.0	30030	0.5172	0.8444
0.3468	144.0	30240	0.5169	0.8431
0.3571	145.0	30450	0.5170	0.8444
0.284	146.0	30660	0.5168	0.8444
0.294	147.0	30870	0.5168	0.8444
0.3104	148.0	31080	0.5167	0.8444
0.3278	149.0	31290	0.5166	0.8444
0.3037	150.0	31500	0.5166	0.8444

Framework versions

Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1

riaindrawati14/vit-transfer-learning

vit-transfer-learning

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

riaindrawati14/
vit-transfer-learning