smids_10x_deit_tiny_rms_00001_fold1

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

• Loss: 0.9553
• Accuracy: 0.9115

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
0.2396	1.0	751	0.2956	0.8932
0.1563	2.0	1502	0.3511	0.8748
0.1378	3.0	2253	0.3209	0.9032
0.0749	4.0	3004	0.4152	0.8965
0.0902	5.0	3755	0.4990	0.8998
0.0311	6.0	4506	0.6972	0.8948
0.0511	7.0	5257	0.6707	0.8915
0.0692	8.0	6008	0.7791	0.8948
0.0655	9.0	6759	0.7801	0.8965
0.0191	10.0	7510	0.8995	0.8948
0.0314	11.0	8261	0.8069	0.8965
0.0092	12.0	9012	0.8789	0.8881
0.0207	13.0	9763	0.9080	0.8915
0.0001	14.0	10514	0.9075	0.8982
0.0137	15.0	11265	1.1216	0.8915
0.0289	16.0	12016	1.0268	0.8932
0.0749	17.0	12767	0.9684	0.8965
0.0004	18.0	13518	0.9374	0.8948
0.0016	19.0	14269	0.9146	0.8998
0.0	20.0	15020	0.8660	0.9115
0.0	21.0	15771	0.8768	0.9132
0.0205	22.0	16522	0.9737	0.8932
0.0	23.0	17273	0.8857	0.9065
0.0	24.0	18024	0.9206	0.9015
0.0	25.0	18775	0.9882	0.9032
0.0051	26.0	19526	0.9311	0.9048
0.0005	27.0	20277	0.9916	0.8831
0.0	28.0	21028	0.8978	0.9065
0.0147	29.0	21779	0.9817	0.8998
0.0	30.0	22530	0.9072	0.9132
0.0	31.0	23281	0.9000	0.9032
0.0	32.0	24032	0.9908	0.9048
0.0	33.0	24783	0.9477	0.8998
0.0001	34.0	25534	0.9361	0.8998
0.0	35.0	26285	0.9285	0.9048
0.0	36.0	27036	0.9622	0.9048
0.0	37.0	27787	0.9080	0.9082
0.0	38.0	28538	1.0318	0.9065
0.0	39.0	29289	0.8954	0.9115
0.0	40.0	30040	0.9047	0.9098
0.0	41.0	30791	0.9568	0.9098
0.0	42.0	31542	0.9648	0.9082
0.0	43.0	32293	0.9575	0.9098
0.0	44.0	33044	0.9498	0.9132
0.0	45.0	33795	0.9583	0.9098
0.0	46.0	34546	0.9523	0.9115
0.0	47.0	35297	0.9525	0.9115
0.0	48.0	36048	0.9545	0.9115
0.0	49.0	36799	0.9543	0.9115
0.0	50.0	37550	0.9553	0.9115

Framework versions

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