smids_10x_deit_tiny_adamax_00001_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: 0.8518
• Accuracy: 0.8933

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
0.292	1.0	750	0.2930	0.885
0.2303	2.0	1500	0.2459	0.9033
0.1782	3.0	2250	0.2507	0.9017
0.1592	4.0	3000	0.2451	0.9
0.1917	5.0	3750	0.2847	0.8967
0.0682	6.0	4500	0.3017	0.895
0.0567	7.0	5250	0.3497	0.8983
0.0573	8.0	6000	0.3610	0.9
0.1186	9.0	6750	0.4077	0.9067
0.0339	10.0	7500	0.4570	0.9017
0.0496	11.0	8250	0.5695	0.8933
0.0132	12.0	9000	0.6133	0.8983
0.0021	13.0	9750	0.5988	0.8933
0.0118	14.0	10500	0.6072	0.8933
0.0074	15.0	11250	0.6482	0.8967
0.0005	16.0	12000	0.7207	0.8933
0.0021	17.0	12750	0.7385	0.885
0.0	18.0	13500	0.7467	0.895
0.0005	19.0	14250	0.7555	0.8967
0.0	20.0	15000	0.7776	0.9
0.0	21.0	15750	0.8136	0.8933
0.0	22.0	16500	0.7839	0.8983
0.0002	23.0	17250	0.7795	0.8983
0.0	24.0	18000	0.7219	0.8967
0.0	25.0	18750	0.7978	0.9
0.0	26.0	19500	0.7801	0.8983
0.0	27.0	20250	0.8617	0.9
0.0	28.0	21000	0.7908	0.8967
0.0	29.0	21750	0.7838	0.9
0.0154	30.0	22500	0.8352	0.895
0.0	31.0	23250	0.8276	0.8967
0.0	32.0	24000	0.8168	0.8983
0.0	33.0	24750	0.8030	0.8933
0.0	34.0	25500	0.8162	0.895
0.0	35.0	26250	0.8097	0.895
0.0	36.0	27000	0.8226	0.8967
0.0047	37.0	27750	0.8297	0.895
0.0	38.0	28500	0.8250	0.8933
0.0	39.0	29250	0.8392	0.8983
0.0	40.0	30000	0.8326	0.9
0.0	41.0	30750	0.8399	0.895
0.0	42.0	31500	0.8505	0.895
0.0	43.0	32250	0.8292	0.8967
0.0	44.0	33000	0.8390	0.895
0.0	45.0	33750	0.8458	0.895
0.0	46.0	34500	0.8491	0.8933
0.0	47.0	35250	0.8489	0.8933
0.0	48.0	36000	0.8502	0.8933
0.0	49.0	36750	0.8534	0.8933
0.0	50.0	37500	0.8518	0.8933

Framework versions

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