smids_10x_deit_small_rms_0001_fold4

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: 1.4626
• Accuracy: 0.885

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
0.2601	1.0	750	0.3716	0.8583
0.1216	2.0	1500	0.6123	0.855
0.1115	3.0	2250	0.5264	0.8667
0.066	4.0	3000	0.5528	0.8733
0.0447	5.0	3750	0.7645	0.8633
0.0487	6.0	4500	0.8232	0.8767
0.0554	7.0	5250	0.7277	0.865
0.0304	8.0	6000	0.8549	0.8667
0.0413	9.0	6750	0.8526	0.865
0.0142	10.0	7500	1.0441	0.8567
0.0561	11.0	8250	1.0164	0.8633
0.0352	12.0	9000	0.8537	0.875
0.0141	13.0	9750	0.9173	0.8617
0.0343	14.0	10500	1.0250	0.86
0.0048	15.0	11250	0.9231	0.8617
0.0038	16.0	12000	1.1476	0.8617
0.0308	17.0	12750	0.9914	0.885
0.0168	18.0	13500	1.0050	0.8783
0.0001	19.0	14250	1.0610	0.8667
0.0	20.0	15000	1.0251	0.86
0.0232	21.0	15750	1.1692	0.855
0.0026	22.0	16500	0.9562	0.8833
0.0001	23.0	17250	1.0914	0.8733
0.0042	24.0	18000	1.0684	0.8767
0.0	25.0	18750	0.9724	0.8833
0.0001	26.0	19500	1.0636	0.86
0.0001	27.0	20250	1.1239	0.86
0.0015	28.0	21000	1.1692	0.8683
0.0308	29.0	21750	1.1241	0.875
0.0263	30.0	22500	1.0816	0.8867
0.0	31.0	23250	0.9644	0.8867
0.0	32.0	24000	1.1653	0.875
0.0	33.0	24750	1.2370	0.8833
0.0117	34.0	25500	1.3585	0.8767
0.0014	35.0	26250	1.1826	0.885
0.0	36.0	27000	1.2030	0.8867
0.0	37.0	27750	1.4012	0.8717
0.0	38.0	28500	1.3242	0.8717
0.0	39.0	29250	1.2640	0.8833
0.0	40.0	30000	1.3613	0.8817
0.0	41.0	30750	1.3467	0.8883
0.0	42.0	31500	1.3505	0.8917
0.0	43.0	32250	1.3990	0.8867
0.0	44.0	33000	1.4081	0.8917
0.0	45.0	33750	1.4275	0.8883
0.0	46.0	34500	1.4373	0.8867
0.0	47.0	35250	1.4471	0.8867
0.0	48.0	36000	1.4562	0.885
0.0	49.0	36750	1.4611	0.885
0.0	50.0	37500	1.4626	0.885

Framework versions

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