smids_5x_deit_tiny_adamax_0001_fold3

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: 0.9118
• Accuracy: 0.8983

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
0.2637	1.0	375	0.4028	0.845
0.1351	2.0	750	0.3297	0.8967
0.0831	3.0	1125	0.4271	0.895
0.0388	4.0	1500	0.5488	0.8933
0.0329	5.0	1875	0.5460	0.91
0.015	6.0	2250	0.7045	0.8983
0.0097	7.0	2625	0.6091	0.9083
0.0008	8.0	3000	0.6613	0.9
0.0051	9.0	3375	0.6977	0.9
0.0095	10.0	3750	0.7759	0.8983
0.0182	11.0	4125	0.8338	0.8983
0.012	12.0	4500	0.7866	0.91
0.0163	13.0	4875	0.7705	0.9067
0.0	14.0	5250	0.8292	0.9033
0.0	15.0	5625	0.7633	0.9167
0.0002	16.0	6000	0.7628	0.9033
0.0	17.0	6375	0.8121	0.91
0.0	18.0	6750	0.7784	0.905
0.0	19.0	7125	0.8065	0.9067
0.0	20.0	7500	0.8530	0.9083
0.0	21.0	7875	0.7859	0.9083
0.0	22.0	8250	0.7653	0.9133
0.0079	23.0	8625	0.8464	0.91
0.0	24.0	9000	0.8244	0.9067
0.0	25.0	9375	0.8204	0.9067
0.0	26.0	9750	0.8559	0.91
0.0	27.0	10125	0.8371	0.9117
0.0	28.0	10500	0.8892	0.9067
0.0	29.0	10875	0.8646	0.9083
0.0054	30.0	11250	0.8291	0.9083
0.0	31.0	11625	0.8780	0.895
0.0	32.0	12000	0.8742	0.9033
0.0	33.0	12375	0.8764	0.9017
0.0	34.0	12750	0.8694	0.9017
0.0	35.0	13125	0.8829	0.9017
0.0	36.0	13500	0.8880	0.905
0.0	37.0	13875	0.8817	0.9
0.0	38.0	14250	0.8874	0.9033
0.0	39.0	14625	0.8863	0.8983
0.0028	40.0	15000	0.8961	0.9017
0.0	41.0	15375	0.8936	0.9017
0.0	42.0	15750	0.9013	0.9017
0.0	43.0	16125	0.9020	0.8983
0.0	44.0	16500	0.9052	0.9
0.0	45.0	16875	0.9070	0.9
0.0	46.0	17250	0.9083	0.9
0.0	47.0	17625	0.9101	0.9
0.0	48.0	18000	0.9113	0.8983
0.0	49.0	18375	0.9121	0.8983
0.0	50.0	18750	0.9118	0.8983

Framework versions

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