hushem_40x_deit_tiny_sgd_00001_fold2

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: 1.4218
• Accuracy: 0.2444

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
1.4676	1.0	215	1.4979	0.2444
1.453	2.0	430	1.4941	0.2444
1.4514	3.0	645	1.4903	0.2444
1.4264	4.0	860	1.4866	0.2222
1.4845	5.0	1075	1.4831	0.2222
1.4049	6.0	1290	1.4797	0.2444
1.408	7.0	1505	1.4764	0.2444
1.4075	8.0	1720	1.4733	0.2444
1.4274	9.0	1935	1.4702	0.2444
1.4165	10.0	2150	1.4673	0.2444
1.3408	11.0	2365	1.4645	0.2444
1.387	12.0	2580	1.4617	0.2444
1.3966	13.0	2795	1.4591	0.2444
1.3631	14.0	3010	1.4566	0.2444
1.3966	15.0	3225	1.4542	0.2444
1.4085	16.0	3440	1.4520	0.2444
1.3593	17.0	3655	1.4498	0.2444
1.3872	18.0	3870	1.4477	0.2444
1.3857	19.0	4085	1.4457	0.2222
1.3961	20.0	4300	1.4439	0.2222
1.3725	21.0	4515	1.4421	0.2222
1.3634	22.0	4730	1.4405	0.2222
1.3404	23.0	4945	1.4389	0.2222
1.2947	24.0	5160	1.4374	0.2222
1.3286	25.0	5375	1.4360	0.2222
1.3597	26.0	5590	1.4346	0.2222
1.3935	27.0	5805	1.4334	0.2222
1.3126	28.0	6020	1.4322	0.2222
1.3862	29.0	6235	1.4311	0.2222
1.345	30.0	6450	1.4301	0.2222
1.3332	31.0	6665	1.4291	0.2222
1.3215	32.0	6880	1.4283	0.2222
1.3753	33.0	7095	1.4274	0.2222
1.3397	34.0	7310	1.4267	0.2222
1.3085	35.0	7525	1.4260	0.2222
1.3414	36.0	7740	1.4254	0.2222
1.3773	37.0	7955	1.4248	0.2222
1.2916	38.0	8170	1.4243	0.2222
1.2953	39.0	8385	1.4238	0.2222
1.3053	40.0	8600	1.4234	0.2222
1.3127	41.0	8815	1.4230	0.2222
1.2816	42.0	9030	1.4227	0.2222
1.3493	43.0	9245	1.4225	0.2222
1.3258	44.0	9460	1.4223	0.2222
1.3441	45.0	9675	1.4221	0.2444
1.2959	46.0	9890	1.4220	0.2444
1.3609	47.0	10105	1.4219	0.2444
1.276	48.0	10320	1.4219	0.2444
1.2931	49.0	10535	1.4218	0.2444
1.3383	50.0	10750	1.4218	0.2444

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2