hushem_40x_deit_small_sgd_00001_fold5

This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 1.5176
• Accuracy: 0.3171

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
1.9948	1.0	220	1.7324	0.2683
1.9668	2.0	440	1.7170	0.2683
1.7569	3.0	660	1.7024	0.2683
1.8204	4.0	880	1.6885	0.2683
1.8992	5.0	1100	1.6754	0.2683
1.8203	6.0	1320	1.6629	0.2683
1.8006	7.0	1540	1.6512	0.2683
1.746	8.0	1760	1.6401	0.2683
1.7509	9.0	1980	1.6297	0.2683
1.7973	10.0	2200	1.6200	0.2683
1.7248	11.0	2420	1.6109	0.2683
1.5895	12.0	2640	1.6025	0.2683
1.6708	13.0	2860	1.5947	0.2683
1.5672	14.0	3080	1.5875	0.2683
1.6734	15.0	3300	1.5810	0.2683
1.6377	16.0	3520	1.5749	0.2683
1.5807	17.0	3740	1.5693	0.2683
1.6065	18.0	3960	1.5643	0.2439
1.5952	19.0	4180	1.5597	0.2439
1.6236	20.0	4400	1.5555	0.2439
1.6357	21.0	4620	1.5517	0.2439
1.5866	22.0	4840	1.5483	0.2439
1.546	23.0	5060	1.5451	0.2439
1.5341	24.0	5280	1.5423	0.2683
1.5615	25.0	5500	1.5397	0.2683
1.5768	26.0	5720	1.5373	0.2683
1.5024	27.0	5940	1.5352	0.2683
1.5377	28.0	6160	1.5332	0.2683
1.5225	29.0	6380	1.5314	0.2683
1.5464	30.0	6600	1.5298	0.2683
1.5869	31.0	6820	1.5284	0.2683
1.5384	32.0	7040	1.5270	0.2683
1.5241	33.0	7260	1.5258	0.2683
1.5029	34.0	7480	1.5247	0.2683
1.4813	35.0	7700	1.5237	0.2927
1.4892	36.0	7920	1.5227	0.2927
1.5014	37.0	8140	1.5219	0.2927
1.5037	38.0	8360	1.5212	0.2927
1.4775	39.0	8580	1.5205	0.2927
1.4967	40.0	8800	1.5200	0.2927
1.4438	41.0	9020	1.5195	0.2927
1.4692	42.0	9240	1.5190	0.2927
1.5023	43.0	9460	1.5187	0.2927
1.4883	44.0	9680	1.5184	0.2927
1.4515	45.0	9900	1.5181	0.2927
1.4741	46.0	10120	1.5179	0.3171
1.4857	47.0	10340	1.5178	0.3171
1.4547	48.0	10560	1.5177	0.3171
1.45	49.0	10780	1.5176	0.3171
1.5056	50.0	11000	1.5176	0.3171

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2