smids_5x_deit_tiny_rms_00001_fold1

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: 0.9495
• Accuracy: 0.8982

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: 1e-05
• 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
0.2593	1.0	376	0.3151	0.8765
0.1718	2.0	752	0.2623	0.8998
0.1615	3.0	1128	0.2861	0.8965
0.0982	4.0	1504	0.3444	0.8881
0.0553	5.0	1880	0.3784	0.9098
0.0747	6.0	2256	0.5204	0.8881
0.0183	7.0	2632	0.5683	0.8948
0.0068	8.0	3008	0.6428	0.8998
0.0727	9.0	3384	0.7962	0.8815
0.0001	10.0	3760	0.7940	0.8965
0.001	11.0	4136	0.9819	0.8681
0.0	12.0	4512	0.8908	0.8848
0.0018	13.0	4888	0.8621	0.8865
0.0198	14.0	5264	0.8948	0.8881
0.0291	15.0	5640	0.9361	0.8915
0.0001	16.0	6016	0.7825	0.8948
0.0	17.0	6392	0.8996	0.8815
0.0001	18.0	6768	0.8212	0.8948
0.0026	19.0	7144	0.8543	0.8831
0.0145	20.0	7520	0.8936	0.8881
0.004	21.0	7896	0.9825	0.8815
0.0	22.0	8272	0.9004	0.8932
0.0001	23.0	8648	0.8961	0.8965
0.0	24.0	9024	1.0000	0.8915
0.0	25.0	9400	0.9507	0.8865
0.079	26.0	9776	1.0040	0.8865
0.0	27.0	10152	0.9365	0.8998
0.0	28.0	10528	0.9689	0.8815
0.0089	29.0	10904	0.9542	0.8898
0.0105	30.0	11280	0.9853	0.8898
0.0	31.0	11656	0.9962	0.8965
0.0	32.0	12032	0.9324	0.8982
0.0	33.0	12408	1.0542	0.8881
0.0	34.0	12784	0.9887	0.8932
0.0	35.0	13160	0.8827	0.9082
0.0	36.0	13536	0.8957	0.8982
0.0	37.0	13912	0.9316	0.8932
0.0	38.0	14288	0.9562	0.8915
0.0	39.0	14664	0.9229	0.8982
0.0	40.0	15040	0.9352	0.8932
0.0	41.0	15416	0.9221	0.8915
0.0	42.0	15792	0.9253	0.8965
0.0	43.0	16168	0.9330	0.8881
0.0	44.0	16544	0.9447	0.8965
0.0	45.0	16920	0.9432	0.8965
0.0047	46.0	17296	0.9445	0.8965
0.0	47.0	17672	0.9464	0.8948
0.0	48.0	18048	0.9465	0.8948
0.0	49.0	18424	0.9475	0.8982
0.0039	50.0	18800	0.9495	0.8982

Framework versions

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