smids_10x_deit_tiny_adamax_001_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: 0.8586
• Accuracy: 0.915

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: 0.001
• 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
0.3782	1.0	750	0.3344	0.8667
0.2904	2.0	1500	0.3574	0.8483
0.2048	3.0	2250	0.3230	0.8817
0.2	4.0	3000	0.3479	0.8933
0.2233	5.0	3750	0.3431	0.8883
0.1334	6.0	4500	0.3350	0.9017
0.1268	7.0	5250	0.3335	0.8967
0.077	8.0	6000	0.4549	0.8883
0.0723	9.0	6750	0.3771	0.9067
0.0426	10.0	7500	0.4455	0.9017
0.0977	11.0	8250	0.4334	0.9067
0.0237	12.0	9000	0.5437	0.9
0.0358	13.0	9750	0.5148	0.885
0.0032	14.0	10500	0.6045	0.9083
0.0293	15.0	11250	0.6394	0.8933
0.0156	16.0	12000	0.6836	0.89
0.0548	17.0	12750	0.5770	0.9017
0.0127	18.0	13500	0.6663	0.8983
0.0203	19.0	14250	0.6791	0.905
0.0154	20.0	15000	0.6990	0.905
0.0128	21.0	15750	0.7251	0.9017
0.0003	22.0	16500	0.7324	0.8933
0.0024	23.0	17250	0.7123	0.9017
0.0015	24.0	18000	0.6502	0.9133
0.0109	25.0	18750	0.6676	0.9117
0.0004	26.0	19500	0.6984	0.9033
0.0105	27.0	20250	0.8181	0.8967
0.0029	28.0	21000	0.7764	0.9
0.0304	29.0	21750	0.7986	0.8967
0.008	30.0	22500	0.8233	0.895
0.0008	31.0	23250	0.8494	0.9033
0.0	32.0	24000	0.8041	0.91
0.0	33.0	24750	0.8842	0.9167
0.0	34.0	25500	0.7437	0.9233
0.0	35.0	26250	0.7405	0.925
0.0	36.0	27000	0.7962	0.9083
0.0059	37.0	27750	0.7867	0.9233
0.0	38.0	28500	0.8151	0.92
0.0	39.0	29250	0.8010	0.91
0.0	40.0	30000	0.8483	0.9133
0.0	41.0	30750	0.8225	0.9167
0.0	42.0	31500	0.8207	0.9167
0.0	43.0	32250	0.8290	0.915
0.0	44.0	33000	0.8408	0.915
0.0	45.0	33750	0.8374	0.9183
0.0	46.0	34500	0.8446	0.9167
0.0	47.0	35250	0.8518	0.915
0.0	48.0	36000	0.8526	0.915
0.0	49.0	36750	0.8568	0.9167
0.0	50.0	37500	0.8586	0.915

Framework versions

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