smids_5x_deit_base_rms_001_fold5

This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.5070
• Accuracy: 0.8067

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
1.0986	1.0	375	1.0985	0.3517
0.9277	2.0	750	0.8963	0.5367
0.8868	3.0	1125	0.8248	0.5567
0.8432	4.0	1500	0.8168	0.5367
0.7756	5.0	1875	0.8234	0.555
0.7692	6.0	2250	0.7291	0.6617
0.7096	7.0	2625	0.7367	0.6533
0.8553	8.0	3000	0.7548	0.63
0.7339	9.0	3375	0.7547	0.6233
0.6755	10.0	3750	0.7150	0.665
0.7434	11.0	4125	0.7034	0.685
0.6966	12.0	4500	0.6998	0.6833
0.6638	13.0	4875	0.8188	0.615
0.7005	14.0	5250	0.6380	0.7233
0.7307	15.0	5625	0.6467	0.7017
0.6252	16.0	6000	0.6189	0.7317
0.6235	17.0	6375	0.5966	0.7267
0.6067	18.0	6750	0.5889	0.7367
0.6586	19.0	7125	0.5888	0.745
0.553	20.0	7500	0.5461	0.7583
0.5457	21.0	7875	0.5458	0.7717
0.535	22.0	8250	0.5661	0.745
0.5802	23.0	8625	0.5673	0.7633
0.585	24.0	9000	0.5456	0.7767
0.5034	25.0	9375	0.5600	0.7517
0.519	26.0	9750	0.5101	0.7767
0.578	27.0	10125	0.5562	0.7517
0.5681	28.0	10500	0.5592	0.7633
0.5613	29.0	10875	0.5207	0.7733
0.4923	30.0	11250	0.5540	0.7683
0.4514	31.0	11625	0.5170	0.795
0.4948	32.0	12000	0.5569	0.775
0.4729	33.0	12375	0.5006	0.7967
0.4583	34.0	12750	0.5008	0.7917
0.4376	35.0	13125	0.4986	0.815
0.3894	36.0	13500	0.5048	0.8033
0.4227	37.0	13875	0.5449	0.7883
0.4237	38.0	14250	0.4850	0.81
0.3609	39.0	14625	0.4881	0.8017
0.4451	40.0	15000	0.5131	0.8067
0.411	41.0	15375	0.5305	0.7983
0.4629	42.0	15750	0.4959	0.8
0.4034	43.0	16125	0.5125	0.8083
0.3681	44.0	16500	0.5034	0.8033
0.4332	45.0	16875	0.4946	0.8017
0.3808	46.0	17250	0.4987	0.8067
0.3828	47.0	17625	0.5113	0.8183
0.2902	48.0	18000	0.5081	0.8
0.3255	49.0	18375	0.5035	0.8083
0.3922	50.0	18750	0.5070	0.8067

Framework versions

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