smids_3x_deit_small_adamax_0001_fold5

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: 0.8808
• Accuracy: 0.9

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
0.3174	1.0	225	0.2998	0.8783
0.1192	2.0	450	0.2723	0.9083
0.0978	3.0	675	0.4594	0.8617
0.0329	4.0	900	0.4368	0.8833
0.0356	5.0	1125	0.4938	0.89
0.0115	6.0	1350	0.5783	0.895
0.0594	7.0	1575	0.5882	0.8933
0.0073	8.0	1800	0.6931	0.8983
0.0161	9.0	2025	0.7537	0.8917
0.0716	10.0	2250	0.8442	0.885
0.0028	11.0	2475	0.9224	0.875
0.0418	12.0	2700	0.8061	0.8883
0.0025	13.0	2925	0.9720	0.865
0.0001	14.0	3150	0.8224	0.8883
0.0003	15.0	3375	0.8506	0.8883
0.0	16.0	3600	0.8357	0.8883
0.0	17.0	3825	0.7488	0.895
0.0043	18.0	4050	0.8378	0.8783
0.008	19.0	4275	0.8339	0.8883
0.0009	20.0	4500	0.8302	0.8917
0.0	21.0	4725	0.8494	0.89
0.0	22.0	4950	0.8420	0.895
0.0	23.0	5175	0.8388	0.8933
0.0042	24.0	5400	0.8556	0.895
0.0	25.0	5625	0.8527	0.895
0.0	26.0	5850	0.8812	0.8917
0.0	27.0	6075	0.8957	0.8917
0.0	28.0	6300	0.8419	0.8933
0.0034	29.0	6525	0.8176	0.8967
0.0	30.0	6750	0.8204	0.9
0.0	31.0	6975	0.8207	0.9
0.0	32.0	7200	0.8298	0.8933
0.0034	33.0	7425	0.8331	0.8967
0.0	34.0	7650	0.8399	0.895
0.0029	35.0	7875	0.8427	0.895
0.0	36.0	8100	0.8464	0.895
0.004	37.0	8325	0.8513	0.8967
0.0	38.0	8550	0.8556	0.8983
0.0	39.0	8775	0.8574	0.8933
0.0	40.0	9000	0.8619	0.8967
0.0	41.0	9225	0.8650	0.8983
0.0	42.0	9450	0.8684	0.8983
0.0027	43.0	9675	0.8709	0.8983
0.0	44.0	9900	0.8740	0.8983
0.0	45.0	10125	0.8773	0.8967
0.0	46.0	10350	0.8777	0.9
0.0	47.0	10575	0.8792	0.9
0.0	48.0	10800	0.8800	0.9
0.0	49.0	11025	0.8807	0.9
0.0	50.0	11250	0.8808	0.9

Framework versions

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