smids_3x_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: 0.6133
• Accuracy: 0.745

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
1.2288	1.0	225	1.2017	0.3733
1.1101	2.0	450	1.1316	0.3717
1.1013	3.0	675	1.0936	0.395
1.0586	4.0	900	1.0632	0.42
1.0359	5.0	1125	1.0361	0.4367
0.9983	6.0	1350	1.0106	0.4633
1.019	7.0	1575	0.9868	0.475
0.9201	8.0	1800	0.9635	0.4933
0.9378	9.0	2025	0.9411	0.5117
0.9004	10.0	2250	0.9202	0.535
0.8802	11.0	2475	0.9003	0.5517
0.9236	12.0	2700	0.8818	0.565
0.8457	13.0	2925	0.8636	0.5817
0.7849	14.0	3150	0.8469	0.5917
0.8073	15.0	3375	0.8311	0.6017
0.8252	16.0	3600	0.8159	0.61
0.7763	17.0	3825	0.8016	0.6267
0.785	18.0	4050	0.7881	0.6333
0.7828	19.0	4275	0.7746	0.64
0.7255	20.0	4500	0.7617	0.6483
0.7412	21.0	4725	0.7495	0.6567
0.7373	22.0	4950	0.7382	0.665
0.7527	23.0	5175	0.7270	0.6667
0.6946	24.0	5400	0.7170	0.67
0.6784	25.0	5625	0.7075	0.6817
0.6623	26.0	5850	0.6988	0.6933
0.6734	27.0	6075	0.6906	0.6983
0.6676	28.0	6300	0.6828	0.7083
0.6739	29.0	6525	0.6758	0.7117
0.6897	30.0	6750	0.6692	0.715
0.6092	31.0	6975	0.6631	0.7167
0.5885	32.0	7200	0.6574	0.7217
0.618	33.0	7425	0.6519	0.7217
0.5886	34.0	7650	0.6471	0.7233
0.587	35.0	7875	0.6427	0.7233
0.6289	36.0	8100	0.6385	0.7233
0.6176	37.0	8325	0.6348	0.7267
0.6111	38.0	8550	0.6313	0.7283
0.5626	39.0	8775	0.6283	0.7317
0.5867	40.0	9000	0.6256	0.735
0.6103	41.0	9225	0.6232	0.7367
0.6145	42.0	9450	0.6210	0.7417
0.5742	43.0	9675	0.6191	0.7417
0.6152	44.0	9900	0.6175	0.745
0.6028	45.0	10125	0.6162	0.745
0.5964	46.0	10350	0.6151	0.745
0.5533	47.0	10575	0.6143	0.745
0.597	48.0	10800	0.6137	0.745
0.617	49.0	11025	0.6134	0.745
0.5745	50.0	11250	0.6133	0.745

Framework versions

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