smids_5x_deit_tiny_rms_00001_fold2

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: 1.2381
• Accuracy: 0.8769

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
0.3659	1.0	375	0.3296	0.8686
0.2195	2.0	750	0.3046	0.8802
0.1328	3.0	1125	0.3414	0.8752
0.0957	4.0	1500	0.3842	0.8802
0.0592	5.0	1875	0.4781	0.8885
0.0554	6.0	2250	0.5329	0.8902
0.0561	7.0	2625	0.7030	0.8735
0.0111	8.0	3000	0.7077	0.8785
0.0138	9.0	3375	0.8845	0.8852
0.0035	10.0	3750	0.8403	0.8819
0.0539	11.0	4125	0.9586	0.8702
0.009	12.0	4500	0.9960	0.8802
0.0001	13.0	4875	1.0306	0.8719
0.0001	14.0	5250	1.0127	0.8835
0.0171	15.0	5625	1.0184	0.8885
0.0071	16.0	6000	0.9932	0.8869
0.0241	17.0	6375	1.0882	0.8752
0.0005	18.0	6750	1.0661	0.8902
0.0877	19.0	7125	1.0148	0.8785
0.0001	20.0	7500	1.0786	0.8735
0.0	21.0	7875	1.0833	0.8852
0.0	22.0	8250	1.1111	0.8785
0.0001	23.0	8625	1.2212	0.8752
0.0	24.0	9000	1.0341	0.8752
0.0	25.0	9375	1.1693	0.8752
0.0	26.0	9750	1.1184	0.8819
0.0	27.0	10125	1.0601	0.8785
0.0009	28.0	10500	1.1933	0.8702
0.0	29.0	10875	1.2058	0.8785
0.0	30.0	11250	1.1743	0.8735
0.0039	31.0	11625	1.2100	0.8785
0.0108	32.0	12000	1.2237	0.8769
0.0031	33.0	12375	1.2193	0.8735
0.0	34.0	12750	1.2009	0.8769
0.0	35.0	13125	1.1695	0.8802
0.0	36.0	13500	1.1623	0.8819
0.0	37.0	13875	1.2497	0.8702
0.0	38.0	14250	1.2770	0.8769
0.0	39.0	14625	1.2424	0.8769
0.0042	40.0	15000	1.2342	0.8819
0.0	41.0	15375	1.2571	0.8785
0.0026	42.0	15750	1.2422	0.8702
0.0032	43.0	16125	1.2321	0.8835
0.0033	44.0	16500	1.2366	0.8852
0.0026	45.0	16875	1.2353	0.8802
0.0	46.0	17250	1.2327	0.8785
0.0046	47.0	17625	1.2346	0.8785
0.0	48.0	18000	1.2359	0.8769
0.0024	49.0	18375	1.2368	0.8769
0.0026	50.0	18750	1.2381	0.8769

Framework versions

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