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: 1.2082
Accuracy: 0.3833

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: 1e-05
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
1.3468	1.0	75	1.3684	0.345
1.2941	2.0	150	1.3595	0.3433
1.2835	3.0	225	1.3508	0.3433
1.3718	4.0	300	1.3426	0.345
1.2334	5.0	375	1.3348	0.345
1.2846	6.0	450	1.3274	0.3467
1.2876	7.0	525	1.3202	0.3483
1.2894	8.0	600	1.3134	0.3483
1.3322	9.0	675	1.3070	0.3483
1.3642	10.0	750	1.3007	0.35
1.2885	11.0	825	1.2947	0.3517
1.2098	12.0	900	1.2891	0.3517
1.2493	13.0	975	1.2838	0.35
1.2305	14.0	1050	1.2787	0.3517
1.2559	15.0	1125	1.2739	0.355
1.216	16.0	1200	1.2692	0.3567
1.2252	17.0	1275	1.2648	0.3583
1.2555	18.0	1350	1.2606	0.36
1.207	19.0	1425	1.2567	0.3583
1.163	20.0	1500	1.2528	0.3583
1.2799	21.0	1575	1.2493	0.3617
1.2576	22.0	1650	1.2460	0.3633
1.259	23.0	1725	1.2428	0.3617
1.2102	24.0	1800	1.2399	0.365
1.206	25.0	1875	1.2370	0.3633
1.2525	26.0	1950	1.2343	0.3683
1.2063	27.0	2025	1.2318	0.3683
1.2191	28.0	2100	1.2294	0.3683
1.2117	29.0	2175	1.2273	0.3683
1.2241	30.0	2250	1.2252	0.37
1.2256	31.0	2325	1.2233	0.3733
1.123	32.0	2400	1.2215	0.3767
1.1778	33.0	2475	1.2198	0.3767
1.2098	34.0	2550	1.2183	0.3817
1.1496	35.0	2625	1.2169	0.3783
1.2108	36.0	2700	1.2156	0.3833
1.2173	37.0	2775	1.2145	0.3817
1.177	38.0	2850	1.2134	0.38
1.1989	39.0	2925	1.2125	0.3783
1.2161	40.0	3000	1.2116	0.3783
1.2506	41.0	3075	1.2109	0.3783
1.2753	42.0	3150	1.2102	0.38
1.215	43.0	3225	1.2097	0.38
1.196	44.0	3300	1.2092	0.38
1.1971	45.0	3375	1.2089	0.3817
1.1869	46.0	3450	1.2086	0.3833
1.1695	47.0	3525	1.2084	0.3833
1.19	48.0	3600	1.2083	0.3833
1.1265	49.0	3675	1.2082	0.3833
1.1801	50.0	3750	1.2082	0.3833

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

hkivancoral/smids_1x_deit_tiny_sgd_00001_fold3

smids_1x_deit_tiny_sgd_00001_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hkivancoral/
smids_1x_deit_tiny_sgd_00001_fold3