hushem_5x_deit_small_rms_001_fold3

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: 1.4338
• Accuracy: 0.5349

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: 0.001
• 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
2.1632	1.0	28	2.6011	0.2558
1.512	2.0	56	1.9238	0.2558
1.4664	3.0	84	1.5930	0.2558
1.4243	4.0	112	1.6311	0.2558
1.4308	5.0	140	1.5023	0.2326
1.3985	6.0	168	1.3885	0.2326
1.6118	7.0	196	1.8250	0.2326
1.4607	8.0	224	1.4482	0.2558
1.4254	9.0	252	1.5210	0.2326
1.2281	10.0	280	1.2713	0.2791
1.1707	11.0	308	1.6980	0.3256
1.1948	12.0	336	1.3889	0.3488
1.0995	13.0	364	1.2122	0.4651
1.0119	14.0	392	1.2109	0.3721
1.025	15.0	420	1.1189	0.4419
0.9953	16.0	448	1.0970	0.5581
1.0322	17.0	476	1.1852	0.5581
1.0805	18.0	504	1.3503	0.4651
1.0129	19.0	532	1.0139	0.5581
0.8769	20.0	560	1.2502	0.5349
0.9527	21.0	588	0.9400	0.6977
0.8714	22.0	616	0.9462	0.6744
0.8727	23.0	644	1.1395	0.4419
0.8037	24.0	672	0.9359	0.5814
0.7753	25.0	700	0.7772	0.6047
0.8041	26.0	728	0.7536	0.6744
0.8222	27.0	756	1.0294	0.4186
0.7867	28.0	784	1.0146	0.6512
0.7746	29.0	812	1.1197	0.5116
0.6826	30.0	840	0.8534	0.6977
0.6952	31.0	868	0.9094	0.5814
0.7133	32.0	896	0.7819	0.6047
0.6818	33.0	924	0.8848	0.6977
0.634	34.0	952	1.0225	0.6047
0.7437	35.0	980	0.9642	0.5349
0.6195	36.0	1008	1.1344	0.6047
0.6464	37.0	1036	1.0624	0.4186
0.5946	38.0	1064	1.1057	0.5116
0.5887	39.0	1092	1.0910	0.6512
0.6287	40.0	1120	1.0898	0.5581
0.5714	41.0	1148	1.2124	0.5349
0.5356	42.0	1176	1.2782	0.5116
0.4544	43.0	1204	1.1905	0.5814
0.3966	44.0	1232	1.4293	0.5349
0.3676	45.0	1260	1.3361	0.5581
0.3673	46.0	1288	1.3624	0.5349
0.3108	47.0	1316	1.3804	0.5581
0.2776	48.0	1344	1.4296	0.5349
0.2985	49.0	1372	1.4338	0.5349
0.271	50.0	1400	1.4338	0.5349

Framework versions

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