hushem_1x_deit_tiny_rms_001_fold5

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: 1.1358
• Accuracy: 0.6098

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
No log	1.0	6	4.7231	0.2683
4.2141	2.0	12	1.8531	0.2683
4.2141	3.0	18	1.6449	0.2439
1.9845	4.0	24	1.4265	0.2439
1.5807	5.0	30	2.0165	0.2439
1.5807	6.0	36	1.5975	0.2683
1.5979	7.0	42	1.4305	0.3171
1.5979	8.0	48	1.4587	0.2683
1.4992	9.0	54	1.2917	0.3171
1.4954	10.0	60	1.2462	0.4390
1.4954	11.0	66	1.2479	0.2683
1.415	12.0	72	1.1246	0.5122
1.415	13.0	78	1.1689	0.4878
1.374	14.0	84	1.3767	0.2927
1.3675	15.0	90	1.1692	0.4146
1.3675	16.0	96	1.6528	0.2927
1.319	17.0	102	1.3151	0.3659
1.319	18.0	108	1.1475	0.4146
1.3335	19.0	114	1.1506	0.3415
1.2819	20.0	120	1.2300	0.3902
1.2819	21.0	126	1.1641	0.4146
1.2507	22.0	132	1.4148	0.3659
1.2507	23.0	138	1.3061	0.3415
1.2134	24.0	144	1.2367	0.3415
1.2611	25.0	150	1.2383	0.4878
1.2611	26.0	156	1.0375	0.4878
1.2053	27.0	162	1.1983	0.4878
1.2053	28.0	168	1.1898	0.4146
1.1593	29.0	174	1.1479	0.4878
1.2426	30.0	180	1.1382	0.5610
1.2426	31.0	186	1.0558	0.5610
1.1866	32.0	192	1.1895	0.4390
1.1866	33.0	198	1.2172	0.4146
1.1453	34.0	204	1.3773	0.4146
1.1026	35.0	210	1.1168	0.5122
1.1026	36.0	216	1.1184	0.5610
1.131	37.0	222	1.1344	0.5366
1.131	38.0	228	1.0932	0.5122
1.1098	39.0	234	1.1070	0.6098
1.0797	40.0	240	1.1237	0.5854
1.0797	41.0	246	1.1366	0.6098
1.0648	42.0	252	1.1358	0.6098
1.0648	43.0	258	1.1358	0.6098
1.0281	44.0	264	1.1358	0.6098
1.0542	45.0	270	1.1358	0.6098
1.0542	46.0	276	1.1358	0.6098
1.0409	47.0	282	1.1358	0.6098
1.0409	48.0	288	1.1358	0.6098
1.0504	49.0	294	1.1358	0.6098
1.0111	50.0	300	1.1358	0.6098

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1