smids_10x_deit_small_rms_0001_fold2

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

• Loss: 1.0878
• Accuracy: 0.8968

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: 0.0001
• 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
0.2199	1.0	750	0.3087	0.8702
0.1415	2.0	1500	0.3270	0.8952
0.0884	3.0	2250	0.4634	0.8735
0.1125	4.0	3000	0.5598	0.8735
0.0577	5.0	3750	0.5910	0.8785
0.0338	6.0	4500	0.6886	0.8802
0.0535	7.0	5250	0.5872	0.8835
0.0197	8.0	6000	0.6155	0.8952
0.0062	9.0	6750	0.7198	0.8852
0.019	10.0	7500	0.6897	0.8985
0.011	11.0	8250	0.6348	0.8985
0.0457	12.0	9000	0.6894	0.8835
0.0048	13.0	9750	0.7305	0.8918
0.0312	14.0	10500	0.8235	0.8835
0.0002	15.0	11250	0.8886	0.8819
0.0307	16.0	12000	0.8040	0.8769
0.0004	17.0	12750	0.6236	0.9068
0.0012	18.0	13500	0.8211	0.8785
0.0004	19.0	14250	0.6599	0.8985
0.0198	20.0	15000	0.6886	0.8985
0.0006	21.0	15750	0.8044	0.8852
0.007	22.0	16500	0.7019	0.8885
0.0333	23.0	17250	0.7287	0.8819
0.0	24.0	18000	1.0716	0.8652
0.0	25.0	18750	0.9627	0.8752
0.0006	26.0	19500	1.0237	0.8686
0.0027	27.0	20250	0.9748	0.8769
0.0004	28.0	21000	0.9776	0.8902
0.0	29.0	21750	0.9254	0.8785
0.0001	30.0	22500	0.9772	0.8902
0.0132	31.0	23250	0.7890	0.9035
0.0002	32.0	24000	0.8329	0.8985
0.0	33.0	24750	0.8259	0.9101
0.0	34.0	25500	0.9870	0.8852
0.0067	35.0	26250	1.0178	0.8918
0.0	36.0	27000	0.9706	0.9035
0.0039	37.0	27750	0.9405	0.8952
0.0	38.0	28500	1.0909	0.8869
0.0	39.0	29250	1.0161	0.9002
0.0	40.0	30000	0.9672	0.9068
0.0	41.0	30750	1.0373	0.8952
0.0	42.0	31500	1.0954	0.8968
0.0	43.0	32250	1.0521	0.9035
0.0	44.0	33000	1.0547	0.8968
0.0028	45.0	33750	1.0695	0.8952
0.0	46.0	34500	1.0676	0.8968
0.0	47.0	35250	1.0805	0.8968
0.0	48.0	36000	1.0830	0.8968
0.0	49.0	36750	1.0856	0.8968
0.0	50.0	37500	1.0878	0.8968

Framework versions

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