smids_5x_deit_base_rms_00001_fold2

This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 1.0612
• Accuracy: 0.8852

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
0.2146	1.0	375	0.2894	0.8952
0.1437	2.0	750	0.3102	0.8935
0.0617	3.0	1125	0.4639	0.8902
0.0286	4.0	1500	0.5331	0.8935
0.0028	5.0	1875	0.6331	0.8802
0.0027	6.0	2250	0.6763	0.8902
0.0012	7.0	2625	0.7498	0.8835
0.0002	8.0	3000	0.6659	0.8952
0.0	9.0	3375	0.7256	0.8935
0.0209	10.0	3750	0.7936	0.8769
0.0042	11.0	4125	0.8361	0.8802
0.001	12.0	4500	0.8162	0.8852
0.0011	13.0	4875	0.8014	0.8968
0.0	14.0	5250	0.8392	0.8885
0.0018	15.0	5625	0.9229	0.8752
0.0	16.0	6000	0.8989	0.8869
0.0029	17.0	6375	0.8923	0.8902
0.0	18.0	6750	0.8680	0.8852
0.0112	19.0	7125	0.9026	0.8852
0.0	20.0	7500	0.9170	0.8902
0.0	21.0	7875	1.0300	0.8735
0.0	22.0	8250	0.8953	0.8885
0.0	23.0	8625	0.9292	0.8918
0.0001	24.0	9000	0.9442	0.8935
0.0	25.0	9375	0.9984	0.8952
0.0	26.0	9750	1.0751	0.8885
0.0	27.0	10125	1.0903	0.8819
0.0	28.0	10500	1.0301	0.8852
0.0	29.0	10875	1.0019	0.8885
0.0	30.0	11250	0.9825	0.8902
0.0045	31.0	11625	1.0018	0.8835
0.0032	32.0	12000	1.0070	0.8885
0.0037	33.0	12375	0.9955	0.8902
0.0	34.0	12750	1.0401	0.8802
0.0	35.0	13125	1.0361	0.8835
0.0	36.0	13500	1.0263	0.8869
0.0	37.0	13875	1.0646	0.8802
0.0	38.0	14250	1.0823	0.8835
0.0	39.0	14625	1.0786	0.8852
0.0029	40.0	15000	1.0585	0.8869
0.0	41.0	15375	1.0567	0.8852
0.0023	42.0	15750	1.0631	0.8852
0.0027	43.0	16125	1.0573	0.8885
0.0026	44.0	16500	1.0579	0.8902
0.0023	45.0	16875	1.0642	0.8852
0.0	46.0	17250	1.0620	0.8852
0.0049	47.0	17625	1.0628	0.8852
0.0	48.0	18000	1.0622	0.8852
0.0022	49.0	18375	1.0616	0.8852
0.0024	50.0	18750	1.0612	0.8852

Framework versions

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