hushem_40x_deit_tiny_sgd_00001_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: 1.4228
• Accuracy: 0.2791

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
1.4808	1.0	217	1.4816	0.2791
1.4823	2.0	434	1.4791	0.2791
1.4134	3.0	651	1.4766	0.2791
1.4759	4.0	868	1.4742	0.2791
1.4883	5.0	1085	1.4718	0.2791
1.4518	6.0	1302	1.4695	0.3023
1.4499	7.0	1519	1.4671	0.2791
1.4363	8.0	1736	1.4648	0.2791
1.4639	9.0	1953	1.4626	0.2791
1.447	10.0	2170	1.4604	0.2791
1.4636	11.0	2387	1.4583	0.3023
1.4249	12.0	2604	1.4562	0.3023
1.4551	13.0	2821	1.4542	0.3023
1.4299	14.0	3038	1.4523	0.2791
1.4254	15.0	3255	1.4505	0.2791
1.3712	16.0	3472	1.4487	0.2791
1.4294	17.0	3689	1.4469	0.2791
1.3982	18.0	3906	1.4452	0.2791
1.39	19.0	4123	1.4437	0.2791
1.3895	20.0	4340	1.4422	0.2791
1.3897	21.0	4557	1.4407	0.2791
1.381	22.0	4774	1.4393	0.2791
1.3878	23.0	4991	1.4380	0.2791
1.4255	24.0	5208	1.4367	0.2791
1.397	25.0	5425	1.4355	0.2791
1.3946	26.0	5642	1.4344	0.2791
1.4141	27.0	5859	1.4334	0.2791
1.391	28.0	6076	1.4324	0.2791
1.3772	29.0	6293	1.4314	0.2791
1.4053	30.0	6510	1.4305	0.2791
1.3414	31.0	6727	1.4297	0.2791
1.368	32.0	6944	1.4288	0.2791
1.3993	33.0	7161	1.4281	0.2791
1.3039	34.0	7378	1.4274	0.2791
1.3467	35.0	7595	1.4268	0.2791
1.3754	36.0	7812	1.4262	0.2791
1.3681	37.0	8029	1.4257	0.2791
1.3927	38.0	8246	1.4252	0.2791
1.3307	39.0	8463	1.4248	0.2791
1.3625	40.0	8680	1.4244	0.2791
1.3775	41.0	8897	1.4240	0.2791
1.3411	42.0	9114	1.4237	0.2791
1.3645	43.0	9331	1.4235	0.2791
1.3775	44.0	9548	1.4233	0.2791
1.3259	45.0	9765	1.4231	0.2791
1.3653	46.0	9982	1.4230	0.2791
1.3629	47.0	10199	1.4229	0.2791
1.3538	48.0	10416	1.4229	0.2791
1.3676	49.0	10633	1.4228	0.2791
1.357	50.0	10850	1.4228	0.2791

Framework versions

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