smids_10x_deit_tiny_adamax_0001_fold5

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: 0.9737
• Accuracy: 0.905

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
0.2005	1.0	750	0.2656	0.9017
0.1545	2.0	1500	0.2591	0.905
0.0978	3.0	2250	0.4162	0.8983
0.0801	4.0	3000	0.4834	0.8967
0.0206	5.0	3750	0.5823	0.8983
0.0031	6.0	4500	0.5789	0.91
0.063	7.0	5250	0.6700	0.905
0.0223	8.0	6000	0.7780	0.9
0.0158	9.0	6750	0.7077	0.91
0.0007	10.0	7500	0.8470	0.9133
0.0055	11.0	8250	0.8576	0.8983
0.0	12.0	9000	0.8487	0.9117
0.0005	13.0	9750	1.0023	0.9
0.0	14.0	10500	0.8480	0.91
0.0003	15.0	11250	0.8518	0.91
0.0	16.0	12000	0.9913	0.9033
0.006	17.0	12750	0.9222	0.9033
0.0005	18.0	13500	0.9286	0.905
0.0012	19.0	14250	0.9239	0.905
0.0	20.0	15000	0.9679	0.9033
0.0004	21.0	15750	0.9229	0.9017
0.0	22.0	16500	0.9725	0.9033
0.0024	23.0	17250	0.9148	0.9133
0.0	24.0	18000	0.9300	0.9117
0.0	25.0	18750	0.9072	0.9067
0.0	26.0	19500	0.9306	0.9017
0.0	27.0	20250	0.8933	0.9117
0.0	28.0	21000	0.8908	0.91
0.0	29.0	21750	0.9728	0.9
0.0036	30.0	22500	0.9189	0.9067
0.0	31.0	23250	0.9100	0.9133
0.0	32.0	24000	0.9330	0.9067
0.0	33.0	24750	0.9244	0.9083
0.0	34.0	25500	0.9397	0.9067
0.0	35.0	26250	0.9397	0.9083
0.0	36.0	27000	0.9534	0.9067
0.0047	37.0	27750	0.9577	0.9067
0.0	38.0	28500	0.9431	0.9067
0.0	39.0	29250	0.9640	0.905
0.0	40.0	30000	0.9546	0.9067
0.0	41.0	30750	0.9625	0.9033
0.0	42.0	31500	0.9609	0.905
0.0	43.0	32250	0.9631	0.905
0.0	44.0	33000	0.9649	0.905
0.0	45.0	33750	0.9667	0.905
0.0	46.0	34500	0.9694	0.905
0.0	47.0	35250	0.9714	0.905
0.0	48.0	36000	0.9719	0.905
0.0	49.0	36750	0.9733	0.905
0.0	50.0	37500	0.9737	0.905

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2