smids_1x_deit_tiny_sgd_00001_fold4

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: 1.1991
• Accuracy: 0.3667

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
1.3246	1.0	75	1.3568	0.3467
1.3026	2.0	150	1.3478	0.3467
1.3075	3.0	225	1.3392	0.3467
1.3769	4.0	300	1.3310	0.3467
1.2997	5.0	375	1.3231	0.3467
1.2661	6.0	450	1.3158	0.3433
1.2888	7.0	525	1.3087	0.3433
1.2851	8.0	600	1.3020	0.3433
1.2991	9.0	675	1.2955	0.3433
1.3514	10.0	750	1.2893	0.345
1.256	11.0	825	1.2833	0.3417
1.2501	12.0	900	1.2778	0.3417
1.2581	13.0	975	1.2726	0.345
1.279	14.0	1050	1.2675	0.345
1.281	15.0	1125	1.2628	0.345
1.2242	16.0	1200	1.2582	0.3483
1.1785	17.0	1275	1.2539	0.3483
1.2882	18.0	1350	1.2497	0.35
1.2177	19.0	1425	1.2459	0.3533
1.1848	20.0	1500	1.2422	0.3567
1.2931	21.0	1575	1.2388	0.3583
1.2179	22.0	1650	1.2355	0.3567
1.2465	23.0	1725	1.2324	0.3567
1.2403	24.0	1800	1.2294	0.355
1.2116	25.0	1875	1.2267	0.355
1.2221	26.0	1950	1.2242	0.36
1.167	27.0	2025	1.2218	0.36
1.2147	28.0	2100	1.2195	0.3583
1.2367	29.0	2175	1.2174	0.355
1.2142	30.0	2250	1.2154	0.355
1.2312	31.0	2325	1.2136	0.3533
1.1773	32.0	2400	1.2119	0.3517
1.1658	33.0	2475	1.2103	0.3517
1.2038	34.0	2550	1.2088	0.355
1.1521	35.0	2625	1.2075	0.3567
1.1878	36.0	2700	1.2062	0.3633
1.2013	37.0	2775	1.2051	0.365
1.1943	38.0	2850	1.2041	0.3633
1.1839	39.0	2925	1.2032	0.3667
1.1836	40.0	3000	1.2024	0.3683
1.1971	41.0	3075	1.2017	0.3683
1.1901	42.0	3150	1.2011	0.365
1.2156	43.0	3225	1.2005	0.365
1.2062	44.0	3300	1.2001	0.365
1.1956	45.0	3375	1.1998	0.365
1.2469	46.0	3450	1.1995	0.3633
1.1737	47.0	3525	1.1993	0.3667
1.1496	48.0	3600	1.1992	0.3667
1.1899	49.0	3675	1.1991	0.3667
1.2185	50.0	3750	1.1991	0.3667

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0