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: 0.6589
Accuracy: 0.8636

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: 2e-05
train_batch_size: 10
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	36	1.5994	0.6364
No log	2.0	72	1.2587	0.6818
No log	3.0	108	1.0993	0.7045
No log	4.0	144	0.9721	0.7955
No log	5.0	180	0.9282	0.7955
No log	6.0	216	0.8947	0.7955
No log	7.0	252	0.8858	0.7727
No log	8.0	288	0.8159	0.7955
No log	9.0	324	0.7772	0.7727
No log	10.0	360	0.7519	0.7955
No log	11.0	396	0.6982	0.7955
No log	12.0	432	0.6639	0.7955
No log	13.0	468	0.6690	0.8409
0.6601	14.0	504	0.6565	0.8409
0.6601	15.0	540	0.6401	0.8409
0.6601	16.0	576	0.5868	0.8864
0.6601	17.0	612	0.5840	0.8864
0.6601	18.0	648	0.6214	0.8409
0.6601	19.0	684	0.6447	0.8636
0.6601	20.0	720	0.6387	0.8409
0.6601	21.0	756	0.5714	0.8636
0.6601	22.0	792	0.5483	0.8864
0.6601	23.0	828	0.5600	0.8864
0.6601	24.0	864	0.5785	0.8864
0.6601	25.0	900	0.5806	0.8864
0.6601	26.0	936	0.5598	0.8636
0.6601	27.0	972	0.5549	0.8864
0.1909	28.0	1008	0.5145	0.8864
0.1909	29.0	1044	0.5294	0.8636
0.1909	30.0	1080	0.5846	0.8636
0.1909	31.0	1116	0.5347	0.8864
0.1909	32.0	1152	0.5251	0.8864
0.1909	33.0	1188	0.5193	0.8864
0.1909	34.0	1224	0.6406	0.8409
0.1909	35.0	1260	0.5039	0.8864
0.1909	36.0	1296	0.5137	0.8864
0.1909	37.0	1332	0.6023	0.8636
0.1909	38.0	1368	0.5625	0.8864
0.1909	39.0	1404	0.5752	0.8864
0.1909	40.0	1440	0.5903	0.8864
0.1909	41.0	1476	0.5143	0.8864
0.0968	42.0	1512	0.5261	0.8864
0.0968	43.0	1548	0.5942	0.8864
0.0968	44.0	1584	0.6026	0.8636
0.0968	45.0	1620	0.5638	0.8864
0.0968	46.0	1656	0.6019	0.8864
0.0968	47.0	1692	0.5953	0.8864
0.0968	48.0	1728	0.6043	0.8864
0.0968	49.0	1764	0.5866	0.8864
0.0968	50.0	1800	0.5090	0.8864
0.0968	51.0	1836	0.5704	0.8864
0.0968	52.0	1872	0.5579	0.8636
0.0968	53.0	1908	0.5058	0.8864
0.0968	54.0	1944	0.5418	0.8864
0.0968	55.0	1980	0.5708	0.8864
0.0656	56.0	2016	0.5818	0.8864
0.0656	57.0	2052	0.5539	0.8864
0.0656	58.0	2088	0.5849	0.8864
0.0656	59.0	2124	0.5396	0.8864
0.0656	60.0	2160	0.5631	0.8864
0.0656	61.0	2196	0.5919	0.8864
0.0656	62.0	2232	0.5955	0.8864
0.0656	63.0	2268	0.5438	0.8864
0.0656	64.0	2304	0.5989	0.8636
0.0656	65.0	2340	0.5062	0.8864
0.0656	66.0	2376	0.5820	0.8636
0.0656	67.0	2412	0.5301	0.8864
0.0656	68.0	2448	0.6138	0.8864
0.0656	69.0	2484	0.5710	0.8636
0.0491	70.0	2520	0.6141	0.8636
0.0491	71.0	2556	0.6304	0.8636
0.0491	72.0	2592	0.5568	0.8636
0.0491	73.0	2628	0.6437	0.8636
0.0491	74.0	2664	0.5329	0.8864
0.0491	75.0	2700	0.6453	0.8864
0.0491	76.0	2736	0.6267	0.8636
0.0491	77.0	2772	0.6246	0.8636
0.0491	78.0	2808	0.6408	0.8636
0.0491	79.0	2844	0.6208	0.8636
0.0491	80.0	2880	0.5944	0.8636
0.0491	81.0	2916	0.6848	0.8636
0.0491	82.0	2952	0.6700	0.8409
0.0491	83.0	2988	0.5625	0.8864
0.0474	84.0	3024	0.4997	0.8864
0.0474	85.0	3060	0.6110	0.8864
0.0474	86.0	3096	0.5661	0.8864
0.0474	87.0	3132	0.5681	0.8864
0.0474	88.0	3168	0.5794	0.8636
0.0474	89.0	3204	0.6098	0.8864
0.0474	90.0	3240	0.6009	0.8636
0.0474	91.0	3276	0.5000	0.8864
0.0474	92.0	3312	0.5285	0.8864
0.0474	93.0	3348	0.5774	0.8864
0.0474	94.0	3384	0.5896	0.8864
0.0474	95.0	3420	0.5478	0.8864
0.0474	96.0	3456	0.5815	0.8864
0.0474	97.0	3492	0.5675	0.8864
0.0393	98.0	3528	0.5773	0.8864
0.0393	99.0	3564	0.6099	0.8864
0.0393	100.0	3600	0.7255	0.8409
0.0393	101.0	3636	0.6300	0.8864
0.0393	102.0	3672	0.5979	0.8409
0.0393	103.0	3708	0.6031	0.8864
0.0393	104.0	3744	0.6200	0.8864
0.0393	105.0	3780	0.6120	0.8864
0.0393	106.0	3816	0.5514	0.9091
0.0393	107.0	3852	0.6425	0.8864
0.0393	108.0	3888	0.6152	0.8864
0.0393	109.0	3924	0.6023	0.8864
0.0393	110.0	3960	0.6170	0.8864
0.0393	111.0	3996	0.6556	0.8864
0.0404	112.0	4032	0.6380	0.8864
0.0404	113.0	4068	0.6216	0.8864
0.0404	114.0	4104	0.5775	0.8864
0.0404	115.0	4140	0.6120	0.8864
0.0404	116.0	4176	0.6221	0.8864
0.0404	117.0	4212	0.6807	0.8636
0.0404	118.0	4248	0.6805	0.8636
0.0404	119.0	4284	0.6660	0.8636
0.0404	120.0	4320	0.6626	0.8636
0.0404	121.0	4356	0.6656	0.8636
0.0404	122.0	4392	0.6151	0.8636
0.0404	123.0	4428	0.6525	0.8636
0.0404	124.0	4464	0.6311	0.8636
0.0268	125.0	4500	0.6375	0.8636
0.0268	126.0	4536	0.6252	0.8636
0.0268	127.0	4572	0.6182	0.8409
0.0268	128.0	4608	0.6195	0.8636
0.0268	129.0	4644	0.6417	0.8636
0.0268	130.0	4680	0.6440	0.8636
0.0268	131.0	4716	0.6726	0.8636
0.0268	132.0	4752	0.6781	0.8636
0.0268	133.0	4788	0.6412	0.8636
0.0268	134.0	4824	0.6514	0.8636
0.0268	135.0	4860	0.6452	0.8636
0.0268	136.0	4896	0.6453	0.8864
0.0268	137.0	4932	0.6408	0.8864
0.0268	138.0	4968	0.6461	0.8864
0.0244	139.0	5004	0.6597	0.8864
0.0244	140.0	5040	0.6539	0.8864
0.0244	141.0	5076	0.6415	0.8864
0.0244	142.0	5112	0.6438	0.8864
0.0244	143.0	5148	0.6581	0.8636
0.0244	144.0	5184	0.6570	0.8636
0.0244	145.0	5220	0.6626	0.8636
0.0244	146.0	5256	0.6622	0.8636
0.0244	147.0	5292	0.6647	0.8636
0.0244	148.0	5328	0.6619	0.8636
0.0244	149.0	5364	0.6591	0.8636
0.0244	150.0	5400	0.6589	0.8636

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1