hushem_1x_deit_tiny_rms_lr0001_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: 1.7300
• Accuracy: 0.6889

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: 0.0001
• 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	3.0363	0.2444
2.6477	2.0	12	1.6954	0.2444
2.6477	3.0	18	1.4980	0.2444
1.482	4.0	24	1.3454	0.3556
1.4166	5.0	30	1.3094	0.4
1.4166	6.0	36	1.6095	0.2444
1.3414	7.0	42	1.9023	0.2444
1.3414	8.0	48	1.3957	0.2222
1.2396	9.0	54	1.1738	0.4
1.2068	10.0	60	1.2312	0.4889
1.2068	11.0	66	1.0903	0.6
0.9263	12.0	72	0.9211	0.5778
0.9263	13.0	78	1.1912	0.4444
0.8539	14.0	84	1.2631	0.5333
0.6672	15.0	90	1.2596	0.5111
0.6672	16.0	96	1.3999	0.4889
0.5299	17.0	102	1.2988	0.5556
0.5299	18.0	108	1.3328	0.5333
0.3853	19.0	114	1.0485	0.6222
0.332	20.0	120	1.1428	0.5778
0.332	21.0	126	1.0486	0.6444
0.1829	22.0	132	1.0866	0.6667
0.1829	23.0	138	1.7727	0.5778
0.111	24.0	144	1.2950	0.6889
0.0444	25.0	150	1.4579	0.7111
0.0444	26.0	156	1.4269	0.6889
0.0017	27.0	162	1.4804	0.6889
0.0017	28.0	168	1.5281	0.6889
0.0007	29.0	174	1.5658	0.6667
0.0005	30.0	180	1.5943	0.6667
0.0005	31.0	186	1.6212	0.6667
0.0004	32.0	192	1.6444	0.6667
0.0004	33.0	198	1.6608	0.6667
0.0003	34.0	204	1.6759	0.6667
0.0003	35.0	210	1.6896	0.6667
0.0003	36.0	216	1.7018	0.6667
0.0003	37.0	222	1.7108	0.6889
0.0003	38.0	228	1.7185	0.6889
0.0003	39.0	234	1.7237	0.6889
0.0002	40.0	240	1.7275	0.6889
0.0002	41.0	246	1.7295	0.6889
0.0003	42.0	252	1.7300	0.6889
0.0003	43.0	258	1.7300	0.6889
0.0002	44.0	264	1.7300	0.6889
0.0002	45.0	270	1.7300	0.6889
0.0002	46.0	276	1.7300	0.6889
0.0002	47.0	282	1.7300	0.6889
0.0002	48.0	288	1.7300	0.6889
0.0002	49.0	294	1.7300	0.6889
0.0002	50.0	300	1.7300	0.6889

Framework versions

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