smids_1x_deit_tiny_adamax_0001_fold4

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: 1.0781
• Accuracy: 0.8733

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
0.437	1.0	75	0.3878	0.8467
0.3579	2.0	150	0.3600	0.8533
0.205	3.0	225	0.4349	0.8517
0.1111	4.0	300	0.4456	0.8633
0.0776	5.0	375	0.5350	0.8483
0.0492	6.0	450	0.6337	0.845
0.046	7.0	525	0.6226	0.8567
0.0111	8.0	600	0.7050	0.8617
0.0753	9.0	675	0.7254	0.8767
0.0006	10.0	750	0.8282	0.8717
0.0291	11.0	825	0.8528	0.87
0.0019	12.0	900	0.8610	0.875
0.0132	13.0	975	0.9173	0.87
0.0003	14.0	1050	0.9559	0.8717
0.0002	15.0	1125	0.9248	0.875
0.0004	16.0	1200	1.0147	0.8717
0.0016	17.0	1275	0.9350	0.8733
0.0151	18.0	1350	1.0934	0.8667
0.0007	19.0	1425	0.9772	0.8733
0.0001	20.0	1500	0.9904	0.8683
0.0007	21.0	1575	1.0162	0.8783
0.0	22.0	1650	1.0249	0.8767
0.0	23.0	1725	1.0244	0.8767
0.0092	24.0	1800	1.0296	0.88
0.0033	25.0	1875	1.0414	0.8767
0.0	26.0	1950	1.0492	0.8783
0.0	27.0	2025	1.0373	0.8733
0.0041	28.0	2100	1.0586	0.8767
0.0098	29.0	2175	1.0478	0.8783
0.0	30.0	2250	1.0448	0.8767
0.0	31.0	2325	1.0423	0.8733
0.0	32.0	2400	1.0519	0.875
0.0	33.0	2475	1.0303	0.875
0.0	34.0	2550	1.0338	0.875
0.0	35.0	2625	1.0649	0.8767
0.0	36.0	2700	1.0482	0.8767
0.0047	37.0	2775	1.0661	0.875
0.0	38.0	2850	1.0529	0.875
0.0	39.0	2925	1.0563	0.875
0.0	40.0	3000	1.0657	0.875
0.0035	41.0	3075	1.0671	0.875
0.0	42.0	3150	1.0658	0.875
0.0	43.0	3225	1.0667	0.8733
0.0	44.0	3300	1.0735	0.875
0.0	45.0	3375	1.0750	0.875
0.0	46.0	3450	1.0752	0.8733
0.0	47.0	3525	1.0774	0.8733
0.0	48.0	3600	1.0784	0.8733
0.0	49.0	3675	1.0781	0.8733
0.0	50.0	3750	1.0781	0.8733

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0