smids_3x_deit_small_adamax_001_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: 1.0171
• Accuracy: 0.8819

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: 0.001
• 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
0.4401	1.0	225	0.3048	0.8735
0.2924	2.0	450	0.3208	0.8702
0.2562	3.0	675	0.3212	0.8702
0.2244	4.0	900	0.3324	0.8735
0.2012	5.0	1125	0.4267	0.8602
0.1372	6.0	1350	0.5512	0.8353
0.1267	7.0	1575	0.3624	0.8869
0.0899	8.0	1800	0.5724	0.8436
0.0839	9.0	2025	0.5571	0.8702
0.0466	10.0	2250	0.5349	0.8569
0.0801	11.0	2475	0.5913	0.8502
0.0671	12.0	2700	0.5854	0.8752
0.0348	13.0	2925	0.6837	0.8552
0.0379	14.0	3150	0.5712	0.8752
0.0439	15.0	3375	0.6348	0.8702
0.003	16.0	3600	0.7977	0.8686
0.0261	17.0	3825	0.6729	0.8735
0.002	18.0	4050	0.7795	0.8636
0.001	19.0	4275	0.6782	0.8852
0.0058	20.0	4500	0.6727	0.8918
0.0166	21.0	4725	0.6389	0.8835
0.0009	22.0	4950	0.7419	0.8752
0.0138	23.0	5175	0.7956	0.8769
0.0224	24.0	5400	0.7981	0.8735
0.0076	25.0	5625	0.7548	0.8802
0.0182	26.0	5850	0.8476	0.8586
0.0001	27.0	6075	0.8394	0.8669
0.0	28.0	6300	0.7756	0.8802
0.0001	29.0	6525	0.8666	0.8752
0.0033	30.0	6750	0.8769	0.8935
0.0046	31.0	6975	0.8571	0.8835
0.0	32.0	7200	0.8804	0.8802
0.0022	33.0	7425	0.8723	0.8902
0.0	34.0	7650	0.8812	0.8869
0.0	35.0	7875	0.9182	0.8819
0.0032	36.0	8100	0.9012	0.8869
0.0	37.0	8325	0.9270	0.8885
0.0	38.0	8550	0.8810	0.8902
0.0032	39.0	8775	0.9223	0.8918
0.0	40.0	9000	0.9755	0.8918
0.0003	41.0	9225	0.9917	0.8885
0.0	42.0	9450	0.9880	0.8869
0.0	43.0	9675	0.9953	0.8869
0.0	44.0	9900	0.9968	0.8869
0.0	45.0	10125	1.0016	0.8869
0.0	46.0	10350	1.0040	0.8852
0.0	47.0	10575	1.0063	0.8835
0.0	48.0	10800	1.0129	0.8835
0.0023	49.0	11025	1.0163	0.8819
0.0023	50.0	11250	1.0171	0.8819

Framework versions

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