vit-base-patch16-224-finetuned-teeth_dataset

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

• Loss: 1.1736
• Accuracy: 0.9348

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.8	3	4.6533	0.0087
No log	1.87	7	4.5848	0.0065
4.6048	2.93	11	4.4608	0.0304
4.6048	4.0	15	4.2857	0.0848
4.6048	4.8	18	4.1470	0.1152
4.2716	5.87	22	3.9641	0.2043
4.2716	6.93	26	3.7705	0.3152
3.7404	8.0	30	3.5809	0.4196
3.7404	8.8	33	3.4766	0.4522
3.7404	9.87	37	3.2981	0.5087
3.1589	10.93	41	3.1132	0.6087
3.1589	12.0	45	2.9494	0.6696
3.1589	12.8	48	2.8361	0.6783
2.6384	13.87	52	2.6521	0.7348
2.6384	14.93	56	2.4943	0.7587
2.1342	16.0	60	2.3422	0.7848
2.1342	16.8	63	2.2327	0.8109
2.1342	17.87	67	2.0834	0.8261
1.714	18.93	71	1.9834	0.8565
1.714	20.0	75	1.8932	0.8674
1.714	20.8	78	1.8618	0.8587
1.4427	21.87	82	1.6974	0.8891
1.4427	22.93	86	1.6663	0.8891
1.1858	24.0	90	1.6014	0.8848
1.1858	24.8	93	1.5112	0.9043
1.1858	25.87	97	1.4732	0.9109
1.0222	26.93	101	1.4304	0.9065
1.0222	28.0	105	1.3915	0.9130
1.0222	28.8	108	1.3509	0.9217
0.8306	29.87	112	1.3054	0.9283
0.8306	30.93	116	1.2870	0.9261
0.7391	32.0	120	1.2645	0.9283
0.7391	32.8	123	1.2454	0.9261
0.7391	33.87	127	1.2395	0.9283
0.6971	34.93	131	1.2076	0.9304
0.6971	36.0	135	1.1821	0.9326
0.6971	36.8	138	1.1736	0.9348
0.6758	37.87	142	1.1671	0.9326
0.6758	38.93	146	1.1656	0.9348
0.6445	40.0	150	1.1649	0.9348

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2