smids_10x_deit_base_adamax_00001_fold1

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

• Loss: 0.5153
• Accuracy: 0.9249

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: 1e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• 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
0.2391	1.0	751	0.2691	0.8881
0.1261	2.0	1502	0.2399	0.9082
0.1129	3.0	2253	0.2368	0.9165
0.0547	4.0	3004	0.2399	0.9215
0.0473	5.0	3755	0.3024	0.9165
0.0084	6.0	4506	0.3345	0.9182
0.001	7.0	5257	0.4000	0.9215
0.0092	8.0	6008	0.4032	0.9182
0.0007	9.0	6759	0.4106	0.9249
0.0001	10.0	7510	0.4482	0.9182
0.0001	11.0	8261	0.4776	0.9182
0.0	12.0	9012	0.4461	0.9215
0.0002	13.0	9763	0.4646	0.9199
0.0	14.0	10514	0.4721	0.9199
0.0	15.0	11265	0.4754	0.9232
0.0	16.0	12016	0.4752	0.9282
0.0	17.0	12767	0.4772	0.9265
0.0	18.0	13518	0.4906	0.9215
0.0	19.0	14269	0.4791	0.9182
0.0	20.0	15020	0.4897	0.9215
0.0	21.0	15771	0.5412	0.9132
0.0	22.0	16522	0.5200	0.9265
0.0	23.0	17273	0.4930	0.9249
0.0	24.0	18024	0.5327	0.9165
0.0	25.0	18775	0.4977	0.9182
0.0	26.0	19526	0.5032	0.9215
0.0	27.0	20277	0.5327	0.9165
0.0	28.0	21028	0.5170	0.9232
0.0022	29.0	21779	0.5055	0.9249
0.0	30.0	22530	0.4999	0.9232
0.0	31.0	23281	0.5556	0.9149
0.0	32.0	24032	0.5049	0.9249
0.0	33.0	24783	0.5110	0.9232
0.0	34.0	25534	0.5596	0.9115
0.0	35.0	26285	0.5071	0.9265
0.0	36.0	27036	0.5052	0.9249
0.0	37.0	27787	0.5090	0.9249
0.0	38.0	28538	0.5107	0.9249
0.0	39.0	29289	0.5094	0.9249
0.0	40.0	30040	0.5107	0.9249
0.0	41.0	30791	0.5100	0.9249
0.0	42.0	31542	0.5114	0.9249
0.0	43.0	32293	0.5123	0.9249
0.0	44.0	33044	0.5134	0.9249
0.0	45.0	33795	0.5146	0.9249
0.0	46.0	34546	0.5165	0.9249
0.0	47.0	35297	0.5154	0.9249
0.0	48.0	36048	0.5153	0.9249
0.0	49.0	36799	0.5157	0.9249
0.0	50.0	37550	0.5153	0.9249

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2