hushem_1x_deit_tiny_rms_001_fold3

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: 1.1536
• Accuracy: 0.4186

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	3.8148	0.2558
4.0682	2.0	12	1.5106	0.2558
4.0682	3.0	18	2.1015	0.2558
1.8697	4.0	24	2.1521	0.2558
1.6204	5.0	30	2.0540	0.2558
1.6204	6.0	36	1.4487	0.2558
1.5557	7.0	42	1.5322	0.2326
1.5557	8.0	48	1.6480	0.2558
1.5276	9.0	54	1.5085	0.2558
1.4446	10.0	60	1.3921	0.2558
1.4446	11.0	66	1.5703	0.2558
1.4728	12.0	72	1.3608	0.2791
1.4728	13.0	78	1.4250	0.3488
1.3652	14.0	84	1.4495	0.2558
1.3593	15.0	90	1.4182	0.3023
1.3593	16.0	96	1.5418	0.3023
1.2943	17.0	102	1.4454	0.3256
1.2943	18.0	108	1.5941	0.3721
1.2915	19.0	114	1.4889	0.2558
1.2591	20.0	120	1.3804	0.3488
1.2591	21.0	126	1.8125	0.2558
1.2263	22.0	132	1.4098	0.3023
1.2263	23.0	138	1.4818	0.2558
1.1885	24.0	144	1.4257	0.3721
1.1814	25.0	150	1.4317	0.3023
1.1814	26.0	156	1.3854	0.3488
1.1163	27.0	162	1.9054	0.3256
1.1163	28.0	168	1.3109	0.3488
1.0609	29.0	174	1.3896	0.3488
1.1038	30.0	180	1.3466	0.3256
1.1038	31.0	186	1.3101	0.3256
1.0099	32.0	192	1.2865	0.3721
1.0099	33.0	198	1.2846	0.3721
1.0297	34.0	204	1.2587	0.4186
0.964	35.0	210	1.2832	0.3953
0.964	36.0	216	1.1929	0.3721
0.9335	37.0	222	1.2162	0.3953
0.9335	38.0	228	1.1906	0.4419
0.8668	39.0	234	1.1859	0.4186
0.8296	40.0	240	1.1516	0.4884
0.8296	41.0	246	1.1577	0.4651
0.8332	42.0	252	1.1536	0.4186
0.8332	43.0	258	1.1536	0.4186
0.8289	44.0	264	1.1536	0.4186
0.8217	45.0	270	1.1536	0.4186
0.8217	46.0	276	1.1536	0.4186
0.8205	47.0	282	1.1536	0.4186
0.8205	48.0	288	1.1536	0.4186
0.8548	49.0	294	1.1536	0.4186
0.8042	50.0	300	1.1536	0.4186

Framework versions

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