smids_5x_deit_tiny_adamax_0001_fold2

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: 1.2333
• Accuracy: 0.8752

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
0.2658	1.0	375	0.3487	0.8686
0.2056	2.0	750	0.3572	0.8802
0.1091	3.0	1125	0.4053	0.8785
0.0803	4.0	1500	0.6864	0.8636
0.0671	5.0	1875	0.6967	0.8702
0.0046	6.0	2250	0.8951	0.8636
0.0027	7.0	2625	0.7926	0.8852
0.0005	8.0	3000	0.7839	0.8769
0.0002	9.0	3375	0.8871	0.8869
0.0002	10.0	3750	0.8009	0.8968
0.0097	11.0	4125	0.9981	0.8669
0.0006	12.0	4500	1.0041	0.8719
0.01	13.0	4875	0.9204	0.8735
0.0001	14.0	5250	0.9628	0.8785
0.036	15.0	5625	0.9459	0.8752
0.0001	16.0	6000	0.9812	0.8819
0.0022	17.0	6375	0.9724	0.8819
0.004	18.0	6750	1.0660	0.8769
0.0	19.0	7125	0.9857	0.8719
0.0	20.0	7500	1.0524	0.8752
0.0	21.0	7875	1.0663	0.8686
0.0	22.0	8250	1.1115	0.8686
0.0	23.0	8625	1.0536	0.8752
0.0	24.0	9000	1.0776	0.8719
0.0	25.0	9375	1.0945	0.8686
0.0	26.0	9750	1.1377	0.8802
0.0	27.0	10125	1.1130	0.8735
0.0	28.0	10500	1.1384	0.8735
0.0	29.0	10875	1.1315	0.8769
0.0	30.0	11250	1.1249	0.8752
0.0037	31.0	11625	1.1445	0.8752
0.0039	32.0	12000	1.1633	0.8719
0.0041	33.0	12375	1.1577	0.8785
0.0	34.0	12750	1.1917	0.8686
0.0	35.0	13125	1.2054	0.8702
0.0	36.0	13500	1.1857	0.8735
0.0	37.0	13875	1.1926	0.8719
0.0	38.0	14250	1.2094	0.8719
0.0	39.0	14625	1.2019	0.8769
0.003	40.0	15000	1.2092	0.8752
0.0	41.0	15375	1.2117	0.8769
0.0026	42.0	15750	1.2195	0.8769
0.0026	43.0	16125	1.2211	0.8752
0.0026	44.0	16500	1.2245	0.8769
0.0024	45.0	16875	1.2259	0.8769
0.0	46.0	17250	1.2293	0.8752
0.0049	47.0	17625	1.2287	0.8752
0.0	48.0	18000	1.2326	0.8752
0.0023	49.0	18375	1.2328	0.8752
0.0024	50.0	18750	1.2333	0.8752

Framework versions

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