smids_10x_deit_small_sgd_0001_fold1

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: 0.4121
• Accuracy: 0.8464

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: 0.0001
• 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
0.9962	1.0	751	1.0240	0.4741
0.8829	2.0	1502	0.9630	0.5259
0.8203	3.0	2253	0.8958	0.6027
0.7637	4.0	3004	0.8327	0.6578
0.7321	5.0	3755	0.7782	0.6912
0.7017	6.0	4506	0.7239	0.7078
0.5812	7.0	5257	0.6809	0.7212
0.581	8.0	6008	0.6410	0.7346
0.5592	9.0	6759	0.6086	0.7513
0.5145	10.0	7510	0.5829	0.7679
0.5332	11.0	8261	0.5629	0.7746
0.4756	12.0	9012	0.5433	0.7796
0.4797	13.0	9763	0.5294	0.7846
0.4315	14.0	10514	0.5168	0.7930
0.4112	15.0	11265	0.5056	0.8013
0.4474	16.0	12016	0.4952	0.8030
0.4529	17.0	12767	0.4868	0.8097
0.421	18.0	13518	0.4802	0.8130
0.4112	19.0	14269	0.4730	0.8130
0.4039	20.0	15020	0.4670	0.8180
0.3219	21.0	15771	0.4615	0.8164
0.411	22.0	16522	0.4563	0.8180
0.3769	23.0	17273	0.4528	0.8214
0.4423	24.0	18024	0.4481	0.8214
0.4214	25.0	18775	0.4442	0.8230
0.4588	26.0	19526	0.4419	0.8280
0.3977	27.0	20277	0.4383	0.8314
0.4288	28.0	21028	0.4359	0.8297
0.3842	29.0	21779	0.4331	0.8331
0.38	30.0	22530	0.4307	0.8331
0.3344	31.0	23281	0.4288	0.8347
0.4273	32.0	24032	0.4264	0.8347
0.3923	33.0	24783	0.4244	0.8364
0.3452	34.0	25534	0.4233	0.8364
0.3666	35.0	26285	0.4214	0.8381
0.3806	36.0	27036	0.4199	0.8397
0.4471	37.0	27787	0.4189	0.8397
0.3236	38.0	28538	0.4183	0.8414
0.2974	39.0	29289	0.4171	0.8397
0.4164	40.0	30040	0.4161	0.8397
0.3819	41.0	30791	0.4153	0.8431
0.3798	42.0	31542	0.4146	0.8447
0.3898	43.0	32293	0.4139	0.8447
0.3508	44.0	33044	0.4133	0.8447
0.3647	45.0	33795	0.4128	0.8447
0.4056	46.0	34546	0.4125	0.8447
0.3591	47.0	35297	0.4123	0.8464
0.4233	48.0	36048	0.4121	0.8464
0.3734	49.0	36799	0.4121	0.8464
0.3779	50.0	37550	0.4121	0.8464

Framework versions

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