smids_10x_deit_small_sgd_0001_fold2

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: 0.4097
• Accuracy: 0.8270

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
0.9968	1.0	750	1.0169	0.4659
0.9174	2.0	1500	0.9543	0.5308
0.8121	3.0	2250	0.8838	0.6273
0.7871	4.0	3000	0.8228	0.6522
0.691	5.0	3750	0.7665	0.6922
0.6733	6.0	4500	0.7184	0.7271
0.611	7.0	5250	0.6739	0.7488
0.5495	8.0	6000	0.6348	0.7537
0.5871	9.0	6750	0.6046	0.7587
0.5362	10.0	7500	0.5781	0.7754
0.5478	11.0	8250	0.5567	0.7754
0.5521	12.0	9000	0.5409	0.7804
0.475	13.0	9750	0.5265	0.7787
0.4124	14.0	10500	0.5147	0.7887
0.4689	15.0	11250	0.5048	0.7870
0.4042	16.0	12000	0.4956	0.7903
0.3787	17.0	12750	0.4873	0.7937
0.4203	18.0	13500	0.4799	0.7937
0.4173	19.0	14250	0.4729	0.7987
0.4444	20.0	15000	0.4676	0.8020
0.4225	21.0	15750	0.4619	0.8020
0.3886	22.0	16500	0.4572	0.8070
0.3882	23.0	17250	0.4523	0.8120
0.3793	24.0	18000	0.4484	0.8103
0.4027	25.0	18750	0.4443	0.8136
0.4864	26.0	19500	0.4411	0.8136
0.4229	27.0	20250	0.4378	0.8153
0.4258	28.0	21000	0.4349	0.8153
0.3905	29.0	21750	0.4322	0.8170
0.4099	30.0	22500	0.4297	0.8170
0.3721	31.0	23250	0.4276	0.8186
0.4104	32.0	24000	0.4255	0.8203
0.3815	33.0	24750	0.4237	0.8220
0.3966	34.0	25500	0.4218	0.8220
0.4057	35.0	26250	0.4202	0.8220
0.4004	36.0	27000	0.4187	0.8220
0.3921	37.0	27750	0.4174	0.8220
0.4046	38.0	28500	0.4161	0.8220
0.3819	39.0	29250	0.4149	0.8220
0.4626	40.0	30000	0.4139	0.8236
0.4062	41.0	30750	0.4130	0.8236
0.3793	42.0	31500	0.4123	0.8253
0.3246	43.0	32250	0.4116	0.8253
0.3382	44.0	33000	0.4110	0.8270
0.3636	45.0	33750	0.4106	0.8270
0.4008	46.0	34500	0.4102	0.8270
0.3708	47.0	35250	0.4099	0.8270
0.3436	48.0	36000	0.4098	0.8270
0.3738	49.0	36750	0.4097	0.8270
0.373	50.0	37500	0.4097	0.8270

Framework versions

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