beit-base-patch16-224-85-fold3

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

• Loss: 0.3297
• Accuracy: 0.9091

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: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	2	0.6425	0.6136
No log	2.0	4	0.6036	0.7273
No log	3.0	6	0.5549	0.7045
No log	4.0	8	0.4879	0.7273
0.5424	5.0	10	0.7873	0.7045
0.5424	6.0	12	0.6817	0.7045
0.5424	7.0	14	0.4846	0.75
0.5424	8.0	16	0.5266	0.7273
0.5424	9.0	18	0.4487	0.7727
0.5207	10.0	20	0.3768	0.8182
0.5207	11.0	22	0.6762	0.7045
0.5207	12.0	24	0.3988	0.8409
0.5207	13.0	26	0.3812	0.8864
0.5207	14.0	28	0.4207	0.7727
0.386	15.0	30	0.3801	0.8182
0.386	16.0	32	0.5402	0.7045
0.386	17.0	34	0.3865	0.8636
0.386	18.0	36	0.3635	0.8182
0.386	19.0	38	0.5374	0.7273
0.3232	20.0	40	0.5088	0.75
0.3232	21.0	42	0.3507	0.8182
0.3232	22.0	44	0.2995	0.8409
0.3232	23.0	46	0.3403	0.8409
0.3232	24.0	48	0.3858	0.8182
0.249	25.0	50	0.4126	0.7727
0.249	26.0	52	0.4907	0.8182
0.249	27.0	54	0.3799	0.8182
0.249	28.0	56	0.3528	0.8182
0.249	29.0	58	0.3775	0.8182
0.2064	30.0	60	0.3520	0.8182
0.2064	31.0	62	0.4397	0.7727
0.2064	32.0	64	0.4284	0.75
0.2064	33.0	66	0.3833	0.8409
0.2064	34.0	68	0.3558	0.8409
0.2066	35.0	70	0.4880	0.8182
0.2066	36.0	72	0.3739	0.8182
0.2066	37.0	74	0.5409	0.7727
0.2066	38.0	76	0.4869	0.8182
0.2066	39.0	78	0.8398	0.75
0.1776	40.0	80	0.5410	0.7955
0.1776	41.0	82	0.4740	0.8409
0.1776	42.0	84	0.3428	0.8636
0.1776	43.0	86	0.3135	0.8864
0.1776	44.0	88	0.3297	0.9091
0.1732	45.0	90	0.3982	0.9091
0.1732	46.0	92	0.5961	0.7955
0.1732	47.0	94	0.4798	0.8864
0.1732	48.0	96	0.5187	0.8182
0.1732	49.0	98	0.4430	0.9091
0.1372	50.0	100	0.4522	0.9091
0.1372	51.0	102	0.5617	0.8409
0.1372	52.0	104	0.6568	0.8182
0.1372	53.0	106	0.8141	0.7955
0.1372	54.0	108	0.6189	0.8409
0.1305	55.0	110	0.5124	0.8636
0.1305	56.0	112	0.5095	0.8636
0.1305	57.0	114	0.4101	0.8864
0.1305	58.0	116	0.7712	0.7727
0.1305	59.0	118	0.5073	0.7955
0.1423	60.0	120	0.3890	0.8636
0.1423	61.0	122	0.5701	0.8182
0.1423	62.0	124	0.5482	0.8409
0.1423	63.0	126	0.5508	0.8409
0.1423	64.0	128	0.6589	0.7955
0.13	65.0	130	0.7184	0.75
0.13	66.0	132	0.4702	0.8864
0.13	67.0	134	0.4339	0.9091
0.13	68.0	136	0.4463	0.9091
0.13	69.0	138	0.4887	0.8864
0.1232	70.0	140	0.5121	0.8636
0.1232	71.0	142	0.4944	0.9091
0.1232	72.0	144	0.5208	0.8864
0.1232	73.0	146	0.6074	0.8182
0.1232	74.0	148	0.8013	0.75
0.1241	75.0	150	0.7022	0.7727
0.1241	76.0	152	0.5641	0.8864
0.1241	77.0	154	0.6550	0.8409
0.1241	78.0	156	0.6268	0.8409
0.1241	79.0	158	0.5466	0.8864
0.1254	80.0	160	0.5453	0.8864
0.1254	81.0	162	0.5663	0.8636
0.1254	82.0	164	0.5377	0.8409
0.1254	83.0	166	0.5381	0.8864
0.1254	84.0	168	0.5459	0.8636
0.1061	85.0	170	0.5490	0.8636
0.1061	86.0	172	0.5444	0.8636
0.1061	87.0	174	0.5344	0.8636
0.1061	88.0	176	0.5251	0.8636
0.1061	89.0	178	0.5178	0.8864
0.1084	90.0	180	0.5161	0.8864
0.1084	91.0	182	0.5184	0.8864
0.1084	92.0	184	0.5185	0.8864
0.1084	93.0	186	0.5300	0.8636
0.1084	94.0	188	0.5599	0.8636
0.1025	95.0	190	0.5972	0.8182
0.1025	96.0	192	0.6083	0.8182
0.1025	97.0	194	0.5969	0.8409
0.1025	98.0	196	0.5769	0.8636
0.1025	99.0	198	0.5673	0.8636
0.1184	100.0	200	0.5642	0.8636

Framework versions

• Transformers 4.40.2
• Pytorch 2.2.1+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1