smids_1x_deit_tiny_sgd_0001_fold4

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: 0.8842
• Accuracy: 0.5767

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
1.2663	1.0	75	1.2852	0.3417
1.1957	2.0	150	1.2221	0.36
1.1637	3.0	225	1.1784	0.3767
1.1755	4.0	300	1.1489	0.365
1.123	5.0	375	1.1280	0.3733
1.103	6.0	450	1.1122	0.3833
1.0888	7.0	525	1.0988	0.3917
1.0793	8.0	600	1.0870	0.4
1.0578	9.0	675	1.0759	0.4133
1.0777	10.0	750	1.0654	0.4183
1.017	11.0	825	1.0553	0.4183
1.0652	12.0	900	1.0460	0.43
1.0325	13.0	975	1.0371	0.4417
1.0323	14.0	1050	1.0281	0.45
1.0256	15.0	1125	1.0196	0.4567
1.0233	16.0	1200	1.0112	0.465
0.9433	17.0	1275	1.0027	0.4667
0.989	18.0	1350	0.9944	0.4733
0.9912	19.0	1425	0.9866	0.4783
0.9822	20.0	1500	0.9787	0.4817
0.9897	21.0	1575	0.9718	0.495
0.9177	22.0	1650	0.9648	0.5067
0.95	23.0	1725	0.9583	0.5133
0.9863	24.0	1800	0.9520	0.515
0.9643	25.0	1875	0.9462	0.5217
0.9026	26.0	1950	0.9408	0.5267
0.9173	27.0	2025	0.9357	0.5283
0.927	28.0	2100	0.9309	0.5333
0.9287	29.0	2175	0.9265	0.5417
0.9309	30.0	2250	0.9222	0.5467
0.9103	31.0	2325	0.9183	0.5483
0.9355	32.0	2400	0.9146	0.5583
0.9176	33.0	2475	0.9110	0.56
0.883	34.0	2550	0.9078	0.5617
0.9163	35.0	2625	0.9049	0.5633
0.8785	36.0	2700	0.9021	0.5667
0.8597	37.0	2775	0.8996	0.565
0.8694	38.0	2850	0.8972	0.5717
0.8945	39.0	2925	0.8952	0.5733
0.8725	40.0	3000	0.8932	0.575
0.875	41.0	3075	0.8915	0.575
0.8552	42.0	3150	0.8899	0.575
0.8903	43.0	3225	0.8886	0.575
0.8534	44.0	3300	0.8873	0.575
0.8746	45.0	3375	0.8863	0.5767
0.8976	46.0	3450	0.8855	0.5767
0.8522	47.0	3525	0.8849	0.5767
0.8759	48.0	3600	0.8845	0.5767
0.9028	49.0	3675	0.8842	0.5767
0.8753	50.0	3750	0.8842	0.5767

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0