tobikoi-classifier-alpha1

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: 0.0002
• Accuracy: 1.0

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: 8
• eval_batch_size: 8
• seed: 1337
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 150.0

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss
0.4014	1.0	54	0.7632	0.3552
0.2253	2.0	108	0.9737	0.1712
0.0768	3.0	162	0.9868	0.0763
0.0694	4.0	216	0.9868	0.0615
0.0433	5.0	270	0.9868	0.0504
0.1045	6.0	324	0.9868	0.0323
0.0148	7.0	378	0.9868	0.0436
0.0156	8.0	432	0.9868	0.0271
0.0109	9.0	486	0.9868	0.0511
0.0142	10.0	540	0.9868	0.0563
0.0307	11.0	594	0.9868	0.0633
0.0092	12.0	648	0.9868	0.0430
0.007	13.0	702	0.9868	0.0508
0.0059	14.0	756	0.9868	0.0598
0.0057	15.0	810	0.9868	0.0639
0.0513	16.0	864	0.9868	0.0579
0.0259	17.0	918	0.9868	0.0707
0.0111	18.0	972	0.9868	0.0611
0.014	19.0	1026	0.9868	0.0620
0.004	20.0	1080	1.0	0.0058
0.0036	21.0	1134	1.0	0.0044
0.0545	22.0	1188	1.0	0.0114
0.0131	23.0	1242	0.9868	0.0621
0.0651	24.0	1296	0.9868	0.0692
0.0047	25.0	1350	1.0	0.0034
0.0374	26.0	1404	1.0	0.0031
0.0482	27.0	1458	1.0	0.0045
0.0026	28.0	1512	1.0	0.0028
0.0038	29.0	1566	1.0	0.0025
0.0027	30.0	1620	1.0	0.0023
0.0145	31.0	1674	0.9868	0.0698
0.0022	32.0	1728	0.9868	0.0255
0.0025	33.0	1782	1.0	0.0095
0.0022	34.0	1836	0.9868	0.0725
0.0019	35.0	1890	0.9868	0.0592
0.0159	36.0	1944	0.9868	0.0747
0.0018	37.0	1998	0.9868	0.0244
0.0016	38.0	2052	1.0	0.0019
0.0017	39.0	2106	1.0	0.0018
0.053	40.0	2160	1.0	0.0023
0.0016	41.0	2214	1.0	0.0061
0.0015	42.0	2268	1.0	0.0102
0.0015	43.0	2322	1.0	0.0019
0.0015	44.0	2376	1.0	0.0062
0.0014	45.0	2430	1.0	0.0014
0.0015	46.0	2484	1.0	0.0015
0.0013	47.0	2538	0.9868	0.0672
0.0012	48.0	2592	1.0	0.0015
0.0012	49.0	2646	0.9868	0.0700
0.0012	50.0	2700	0.9868	0.0579
0.0011	51.0	2754	0.9868	0.0571
0.001	52.0	2808	0.9868	0.0670
0.001	53.0	2862	0.9868	0.0730
0.0013	54.0	2916	0.9868	0.0135
0.001	55.0	2970	0.9868	0.0836
0.0009	56.0	3024	1.0	0.0010
0.0009	57.0	3078	0.9868	0.0122
0.001	58.0	3132	0.9868	0.0105
0.0017	59.0	3186	1.0	0.0074
0.0009	60.0	3240	1.0	0.0010
0.0009	61.0	3294	1.0	0.0009
0.0381	62.0	3348	1.0	0.0020
0.0008	63.0	3402	1.0	0.0008
0.0099	64.0	3456	1.0	0.0008
0.0007	65.0	3510	0.9868	0.0757
0.0008	66.0	3564	0.9868	0.0764
0.0007	67.0	3618	0.9737	0.1257
0.0007	68.0	3672	0.9868	0.0098
0.0736	69.0	3726	1.0	0.0008
0.0007	70.0	3780	0.9868	0.0605
0.0006	71.0	3834	1.0	0.0012
0.001	72.0	3888	0.9737	0.1666
0.0042	73.0	3942	1.0	0.0007
0.0006	74.0	3996	1.0	0.0007
0.0007	75.0	4050	1.0	0.0007
0.0006	76.0	4104	0.9868	0.0331
0.0006	77.0	4158	0.9868	0.0169
0.0345	78.0	4212	1.0	0.0006
0.0005	79.0	4266	0.9868	0.0762
0.0005	80.0	4320	1.0	0.0007
0.0005	81.0	4374	1.0	0.0005
0.0005	82.0	4428	1.0	0.0006
0.0005	83.0	4482	1.0	0.0005
0.0005	84.0	4536	1.0	0.0005
0.0047	85.0	4590	1.0	0.0007
0.0005	86.0	4644	1.0	0.0005
0.0005	87.0	4698	1.0	0.0005
0.0004	88.0	4752	1.0	0.0004
0.0004	89.0	4806	1.0	0.0004
0.0005	90.0	4860	1.0	0.0005
0.0004	91.0	4914	1.0	0.0005
0.0067	92.0	4968	1.0	0.0004
0.0004	93.0	5022	1.0	0.0004
0.0004	94.0	5076	1.0	0.0004
0.0004	95.0	5130	1.0	0.0004
0.0004	96.0	5184	1.0	0.0004
0.0004	97.0	5238	1.0	0.0004
0.0004	98.0	5292	1.0	0.0004
0.0003	99.0	5346	1.0	0.0004
0.0003	100.0	5400	1.0	0.0003
0.0003	101.0	5454	1.0	0.0004
0.0004	102.0	5508	1.0	0.0005
0.0004	103.0	5562	1.0	0.0005
0.0004	104.0	5616	1.0	0.0004
0.0006	105.0	5670	1.0	0.0003
0.0005	106.0	5724	1.0	0.0003
0.0003	107.0	5778	1.0	0.0003
0.0003	108.0	5832	1.0	0.0003
0.0003	109.0	5886	1.0	0.0003
0.0003	110.0	5940	1.0	0.0003
0.0003	111.0	5994	1.0	0.0003
0.0003	112.0	6048	1.0	0.0003
0.0003	113.0	6102	1.0	0.0003
0.0003	114.0	6156	1.0	0.0003
0.0003	115.0	6210	1.0	0.0003
0.0003	116.0	6264	1.0	0.0003
0.0003	117.0	6318	1.0	0.0003
0.0003	118.0	6372	1.0	0.0003
0.0002	119.0	6426	1.0	0.0002
0.0002	120.0	6480	1.0	0.0002
0.0002	121.0	6534	1.0	0.0002
0.0003	122.0	6588	1.0	0.0002
0.0002	123.0	6642	1.0	0.0002
0.0002	124.0	6696	1.0	0.0002
0.0002	125.0	6750	1.0	0.0002
0.0002	126.0	6804	1.0	0.0002
0.0712	127.0	6858	1.0	0.0002
0.0002	128.0	6912	1.0	0.0002
0.0002	129.0	6966	1.0	0.0002
0.0002	130.0	7020	1.0	0.0002
0.0002	131.0	7074	1.0	0.0002
0.0002	132.0	7128	1.0	0.0002
0.0002	133.0	7182	1.0	0.0002
0.0002	134.0	7236	1.0	0.0002
0.0002	135.0	7290	1.0	0.0002
0.0003	136.0	7344	1.0	0.0002
0.0002	137.0	7398	1.0	0.0002
0.0002	138.0	7452	1.0	0.0002
0.0028	139.0	7506	1.0	0.0002
0.0006	140.0	7560	1.0	0.0002
0.0002	141.0	7614	1.0	0.0002
0.0002	142.0	7668	1.0	0.0002
0.0004	143.0	7722	1.0	0.0002
0.0002	144.0	7776	1.0	0.0002
0.0002	145.0	7830	1.0	0.0002
0.1028	146.0	7884	1.0	0.0002
0.0002	147.0	7938	1.0	0.0002
0.0002	148.0	7992	1.0	0.0002
0.0002	149.0	8046	1.0	0.0002
0.0002	150.0	8100	1.0	0.0002

Framework versions

• Transformers 4.36.0.dev0
• Pytorch 2.1.1+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0