hushem_1x_deit_tiny_sgd_lr001_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: 1.3137
• 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	1.5163	0.3023
1.6001	2.0	12	1.4936	0.3023
1.6001	3.0	18	1.4729	0.3023
1.5411	4.0	24	1.4550	0.3023
1.4977	5.0	30	1.4401	0.3023
1.4977	6.0	36	1.4267	0.3023
1.4396	7.0	42	1.4159	0.3023
1.4396	8.0	48	1.4066	0.3023
1.4314	9.0	54	1.3991	0.3023
1.3704	10.0	60	1.3909	0.3023
1.3704	11.0	66	1.3847	0.3023
1.3552	12.0	72	1.3793	0.3023
1.3552	13.0	78	1.3735	0.3256
1.3421	14.0	84	1.3686	0.3488
1.3202	15.0	90	1.3638	0.3488
1.3202	16.0	96	1.3593	0.3721
1.2948	17.0	102	1.3558	0.3953
1.2948	18.0	108	1.3518	0.3953
1.2928	19.0	114	1.3488	0.3953
1.2647	20.0	120	1.3454	0.3953
1.2647	21.0	126	1.3427	0.3953
1.2556	22.0	132	1.3402	0.3953
1.2556	23.0	138	1.3379	0.3953
1.253	24.0	144	1.3353	0.3953
1.2437	25.0	150	1.3327	0.3953
1.2437	26.0	156	1.3306	0.4186
1.2239	27.0	162	1.3289	0.3953
1.2239	28.0	168	1.3270	0.3953
1.2275	29.0	174	1.3251	0.3953
1.2028	30.0	180	1.3234	0.3953
1.2028	31.0	186	1.3221	0.3953
1.202	32.0	192	1.3205	0.3953
1.202	33.0	198	1.3191	0.3953
1.194	34.0	204	1.3178	0.3953
1.1993	35.0	210	1.3169	0.4186
1.1993	36.0	216	1.3160	0.4186
1.1904	37.0	222	1.3153	0.4186
1.1904	38.0	228	1.3147	0.4186
1.1785	39.0	234	1.3142	0.4186
1.2086	40.0	240	1.3139	0.4186
1.2086	41.0	246	1.3138	0.4186
1.1893	42.0	252	1.3137	0.4186
1.1893	43.0	258	1.3137	0.4186
1.2	44.0	264	1.3137	0.4186
1.1775	45.0	270	1.3137	0.4186
1.1775	46.0	276	1.3137	0.4186
1.1852	47.0	282	1.3137	0.4186
1.1852	48.0	288	1.3137	0.4186
1.1783	49.0	294	1.3137	0.4186
1.1702	50.0	300	1.3137	0.4186

Framework versions

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