smids_3x_deit_tiny_rms_0001_fold2

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: 1.0507
• Accuracy: 0.8885

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
0.3654	1.0	225	0.3820	0.8419
0.2564	2.0	450	0.3888	0.8502
0.2078	3.0	675	0.3453	0.8669
0.1779	4.0	900	0.3342	0.8785
0.0923	5.0	1125	0.4468	0.8702
0.1133	6.0	1350	0.4712	0.8885
0.1075	7.0	1575	0.5119	0.8785
0.0546	8.0	1800	0.5949	0.8852
0.107	9.0	2025	0.6816	0.8619
0.0417	10.0	2250	0.6436	0.8918
0.0434	11.0	2475	0.6287	0.8918
0.0459	12.0	2700	0.7263	0.8802
0.01	13.0	2925	1.0463	0.8586
0.0718	14.0	3150	0.7632	0.8686
0.0139	15.0	3375	0.8074	0.8752
0.0175	16.0	3600	0.9064	0.8819
0.0398	17.0	3825	0.8900	0.8569
0.0628	18.0	4050	0.8666	0.8769
0.0021	19.0	4275	1.0191	0.8636
0.0486	20.0	4500	0.9743	0.8619
0.0363	21.0	4725	0.8658	0.8636
0.0187	22.0	4950	0.8042	0.8802
0.061	23.0	5175	0.9235	0.8735
0.0107	24.0	5400	0.9113	0.8752
0.0112	25.0	5625	1.0185	0.8785
0.0001	26.0	5850	0.9687	0.8636
0.0001	27.0	6075	0.8990	0.8686
0.0	28.0	6300	0.8022	0.8735
0.0001	29.0	6525	0.9932	0.8752
0.0056	30.0	6750	0.9438	0.8785
0.0025	31.0	6975	0.8626	0.8719
0.0001	32.0	7200	0.8253	0.8835
0.0037	33.0	7425	0.8896	0.8952
0.0001	34.0	7650	0.8932	0.8785
0.0037	35.0	7875	0.9625	0.8885
0.0037	36.0	8100	0.9054	0.8869
0.0	37.0	8325	0.9088	0.8802
0.0	38.0	8550	1.0141	0.8719
0.0067	39.0	8775	1.0333	0.8902
0.0	40.0	9000	0.9904	0.8802
0.0007	41.0	9225	1.0454	0.8802
0.0	42.0	9450	1.0162	0.8835
0.0	43.0	9675	1.0365	0.8819
0.0	44.0	9900	1.0455	0.8819
0.0	45.0	10125	1.0251	0.8852
0.0	46.0	10350	1.0400	0.8902
0.0	47.0	10575	1.0402	0.8869
0.0	48.0	10800	1.0455	0.8852
0.0025	49.0	11025	1.0501	0.8885
0.0025	50.0	11250	1.0507	0.8885

Framework versions

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