smids_3x_deit_tiny_rms_00001_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: 1.3270
• Accuracy: 0.8783

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
0.3199	1.0	225	0.3628	0.8533
0.1542	2.0	450	0.3942	0.8633
0.1186	3.0	675	0.4019	0.8783
0.1068	4.0	900	0.4521	0.8667
0.0629	5.0	1125	0.5068	0.8733
0.0347	6.0	1350	0.6175	0.8733
0.0294	7.0	1575	0.7043	0.8683
0.0692	8.0	1800	0.7322	0.8683
0.0709	9.0	2025	0.8957	0.8683
0.0668	10.0	2250	0.9618	0.8633
0.0043	11.0	2475	1.0931	0.855
0.0053	12.0	2700	1.0669	0.8617
0.0043	13.0	2925	1.2308	0.8533
0.0002	14.0	3150	1.1863	0.86
0.0021	15.0	3375	1.1504	0.8633
0.0	16.0	3600	1.1831	0.8683
0.0542	17.0	3825	1.2418	0.8633
0.0002	18.0	4050	1.2334	0.8567
0.0	19.0	4275	1.2760	0.8717
0.0018	20.0	4500	1.3796	0.8567
0.0	21.0	4725	1.2762	0.8683
0.0	22.0	4950	1.3847	0.8583
0.0001	23.0	5175	1.2718	0.8733
0.0	24.0	5400	1.2359	0.8717
0.0	25.0	5625	1.1935	0.8783
0.0	26.0	5850	1.2054	0.8783
0.0	27.0	6075	1.3706	0.86
0.0	28.0	6300	1.3639	0.8683
0.0	29.0	6525	1.2958	0.865
0.0	30.0	6750	1.3600	0.8617
0.0001	31.0	6975	1.4078	0.865
0.0	32.0	7200	1.3542	0.8667
0.0	33.0	7425	1.3120	0.865
0.0	34.0	7650	1.2757	0.8717
0.0	35.0	7875	1.2928	0.8667
0.0	36.0	8100	1.3460	0.8667
0.0002	37.0	8325	1.3775	0.8633
0.0	38.0	8550	1.3704	0.8617
0.0	39.0	8775	1.3338	0.87
0.0	40.0	9000	1.3261	0.8717
0.0	41.0	9225	1.3217	0.8717
0.0068	42.0	9450	1.3066	0.8733
0.0	43.0	9675	1.3141	0.875
0.0059	44.0	9900	1.3176	0.8767
0.0	45.0	10125	1.3464	0.865
0.0	46.0	10350	1.3263	0.8733
0.0	47.0	10575	1.3244	0.875
0.0	48.0	10800	1.3245	0.8767
0.0	49.0	11025	1.3277	0.8783
0.0	50.0	11250	1.3270	0.8783

Framework versions

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