smids_3x_deit_small_rms_00001_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: 1.2809
• Accuracy: 0.8686

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
0.2767	1.0	225	0.3185	0.8686
0.1511	2.0	450	0.3126	0.8669
0.096	3.0	675	0.3591	0.8735
0.0576	4.0	900	0.4235	0.8735
0.0262	5.0	1125	0.4922	0.8819
0.0164	6.0	1350	0.6380	0.8719
0.0169	7.0	1575	0.7607	0.8569
0.0085	8.0	1800	0.8747	0.8602
0.0061	9.0	2025	0.9757	0.8586
0.002	10.0	2250	1.0480	0.8586
0.0017	11.0	2475	0.9765	0.8702
0.0016	12.0	2700	0.9383	0.8719
0.0018	13.0	2925	0.9688	0.8719
0.0	14.0	3150	0.9770	0.8602
0.0002	15.0	3375	0.9981	0.8686
0.0	16.0	3600	0.9902	0.8735
0.0075	17.0	3825	1.0861	0.8586
0.0092	18.0	4050	1.0830	0.8552
0.0002	19.0	4275	0.9892	0.8719
0.0029	20.0	4500	1.1768	0.8619
0.0	21.0	4725	1.1820	0.8619
0.031	22.0	4950	1.0285	0.8619
0.0053	23.0	5175	1.0925	0.8569
0.0	24.0	5400	1.1089	0.8652
0.0412	25.0	5625	1.2047	0.8502
0.0	26.0	5850	1.1861	0.8569
0.0	27.0	6075	1.2680	0.8569
0.0001	28.0	6300	1.1737	0.8652
0.0173	29.0	6525	1.2944	0.8486
0.0044	30.0	6750	1.1884	0.8636
0.0	31.0	6975	1.2534	0.8652
0.0	32.0	7200	1.2427	0.8636
0.0	33.0	7425	1.2253	0.8719
0.0	34.0	7650	1.2543	0.8652
0.0	35.0	7875	1.2431	0.8702
0.004	36.0	8100	1.2651	0.8619
0.0	37.0	8325	1.2443	0.8652
0.0	38.0	8550	1.2852	0.8669
0.004	39.0	8775	1.2690	0.8686
0.0	40.0	9000	1.2725	0.8686
0.0	41.0	9225	1.2668	0.8719
0.0	42.0	9450	1.2758	0.8686
0.0	43.0	9675	1.2725	0.8669
0.0	44.0	9900	1.2814	0.8669
0.0	45.0	10125	1.2808	0.8686
0.0	46.0	10350	1.2792	0.8702
0.0	47.0	10575	1.2803	0.8686
0.0	48.0	10800	1.2804	0.8686
0.0022	49.0	11025	1.2805	0.8686
0.0022	50.0	11250	1.2809	0.8686

Framework versions

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