smids_10x_deit_small_rms_00001_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: 1.1223
• Accuracy: 0.8952

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
0.2125	1.0	750	0.2971	0.8636
0.1007	2.0	1500	0.3569	0.8902
0.033	3.0	2250	0.4786	0.8852
0.0414	4.0	3000	0.6308	0.8719
0.0169	5.0	3750	0.7881	0.8769
0.0209	6.0	4500	0.8756	0.8802
0.0232	7.0	5250	0.7942	0.8785
0.0001	8.0	6000	0.8024	0.8885
0.0037	9.0	6750	0.9766	0.8852
0.0663	10.0	7500	0.9288	0.8785
0.0416	11.0	8250	1.0051	0.8835
0.0257	12.0	9000	1.1036	0.8752
0.0003	13.0	9750	0.9284	0.8835
0.0007	14.0	10500	0.9766	0.8752
0.0009	15.0	11250	1.0060	0.8869
0.024	16.0	12000	0.9566	0.8918
0.0002	17.0	12750	0.9308	0.8985
0.0226	18.0	13500	0.9878	0.8952
0.0002	19.0	14250	1.0344	0.8802
0.0	20.0	15000	1.0012	0.8902
0.0	21.0	15750	1.0757	0.8852
0.0197	22.0	16500	1.1327	0.8918
0.0059	23.0	17250	1.1959	0.8785
0.014	24.0	18000	0.9244	0.8918
0.0	25.0	18750	1.0134	0.8952
0.0001	26.0	19500	1.2273	0.8735
0.0081	27.0	20250	1.2216	0.8735
0.0	28.0	21000	1.1304	0.8769
0.0	29.0	21750	0.9499	0.8902
0.0	30.0	22500	1.0368	0.8885
0.0	31.0	23250	1.0392	0.8852
0.0038	32.0	24000	1.2288	0.8835
0.0	33.0	24750	1.1678	0.8952
0.0	34.0	25500	1.0162	0.8918
0.0	35.0	26250	1.0770	0.8918
0.0	36.0	27000	1.0678	0.8902
0.0067	37.0	27750	1.0739	0.8935
0.0	38.0	28500	1.1577	0.8935
0.0	39.0	29250	1.1277	0.8935
0.0	40.0	30000	1.1396	0.8918
0.0	41.0	30750	1.1296	0.8952
0.0	42.0	31500	1.1324	0.8935
0.0	43.0	32250	1.1390	0.8918
0.0	44.0	33000	1.1307	0.8952
0.0025	45.0	33750	1.1302	0.8918
0.0	46.0	34500	1.1293	0.8935
0.0	47.0	35250	1.1264	0.8935
0.0	48.0	36000	1.1267	0.8952
0.0	49.0	36750	1.1233	0.8952
0.0	50.0	37500	1.1223	0.8952

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2