smids_3x_deit_small_adamax_0001_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: 1.0636
• Accuracy: 0.8769

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
0.2927	1.0	225	0.3073	0.8686
0.1512	2.0	450	0.3158	0.8902
0.0739	3.0	675	0.4826	0.8602
0.0696	4.0	900	0.4520	0.8835
0.0095	5.0	1125	0.6442	0.8636
0.0162	6.0	1350	0.8340	0.8403
0.0336	7.0	1575	0.6936	0.8669
0.0025	8.0	1800	0.8353	0.8669
0.0105	9.0	2025	0.8036	0.8702
0.0025	10.0	2250	0.8245	0.8769
0.0004	11.0	2475	0.9095	0.8802
0.0003	12.0	2700	1.0840	0.8669
0.0001	13.0	2925	0.9475	0.8735
0.016	14.0	3150	0.9122	0.8835
0.0	15.0	3375	0.7790	0.8918
0.0	16.0	3600	0.7787	0.8785
0.0029	17.0	3825	0.8308	0.8918
0.0	18.0	4050	0.9812	0.8752
0.0006	19.0	4275	0.9540	0.8686
0.0	20.0	4500	0.9292	0.8785
0.0	21.0	4725	0.9652	0.8785
0.0	22.0	4950	0.9762	0.8802
0.0029	23.0	5175	0.9826	0.8702
0.0	24.0	5400	0.9763	0.8669
0.0047	25.0	5625	0.9733	0.8719
0.0	26.0	5850	0.9680	0.8686
0.0	27.0	6075	0.9775	0.8869
0.0	28.0	6300	0.9726	0.8769
0.0	29.0	6525	1.0293	0.8702
0.0036	30.0	6750	1.0029	0.8885
0.0	31.0	6975	0.9992	0.8686
0.0	32.0	7200	0.9937	0.8686
0.0	33.0	7425	0.9950	0.8802
0.0	34.0	7650	0.9997	0.8735
0.0	35.0	7875	1.0478	0.8769
0.0033	36.0	8100	1.0278	0.8686
0.0	37.0	8325	1.0247	0.8802
0.0	38.0	8550	1.0317	0.8785
0.0032	39.0	8775	1.0297	0.8819
0.0	40.0	9000	1.0396	0.8802
0.0	41.0	9225	1.0404	0.8752
0.0	42.0	9450	1.0476	0.8785
0.0	43.0	9675	1.0513	0.8802
0.0	44.0	9900	1.0502	0.8769
0.0	45.0	10125	1.0584	0.8752
0.0	46.0	10350	1.0568	0.8769
0.0	47.0	10575	1.0576	0.8785
0.0	48.0	10800	1.0616	0.8785
0.0023	49.0	11025	1.0626	0.8769
0.0023	50.0	11250	1.0636	0.8769

Framework versions

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