ViT_ASVspoof_DF

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

• Loss: 1.8822
• Accuracy: 0.8934
• F1: 0.8431
• Precision: 0.7982
• Recall: 0.8934
• Test: 1
• Auc Roc: 0.3976

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: 128
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 2
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Test	Auc Roc
0.3293	0.1078	50	0.5369	0.8934	0.8431	0.7982	0.8934	1	0.4810
0.1251	0.2155	100	0.7074	0.8934	0.8431	0.7982	0.8934	1	0.5209
0.0671	0.3233	150	0.8683	0.8934	0.8431	0.7982	0.8934	1	0.5390
0.0463	0.4310	200	0.8867	0.8934	0.8431	0.7982	0.8934	1	0.5820
0.0365	0.5388	250	0.9675	0.8934	0.8431	0.7982	0.8934	1	0.6129
0.0332	0.6466	300	1.1225	0.8934	0.8431	0.7982	0.8934	1	0.5544
0.0788	0.7543	350	1.1081	0.8934	0.8431	0.7982	0.8934	1	0.5776
0.0425	0.8621	400	1.4392	0.8934	0.8431	0.7982	0.8934	1	0.5835
0.0566	0.9698	450	1.8030	0.8934	0.8431	0.7982	0.8934	1	0.5043
0.0821	1.0776	500	1.8901	0.8934	0.8431	0.7982	0.8934	1	0.6352
0.1122	1.1853	550	1.8085	0.8934	0.8431	0.7982	0.8934	1	0.3735
0.0446	1.2931	600	1.9759	0.8934	0.8431	0.7982	0.8934	1	0.3383
0.0342	1.4009	650	1.9482	0.8934	0.8431	0.7982	0.8934	1	0.4254
0.028	1.5086	700	1.9181	0.8934	0.8431	0.7982	0.8934	1	0.3508
0.0195	1.6164	750	1.9146	0.8934	0.8431	0.7982	0.8934	1	0.4860
0.0107	1.7241	800	1.8752	0.8934	0.8431	0.7982	0.8934	1	0.4285
0.0092	1.8319	850	1.8792	0.8934	0.8431	0.7982	0.8934	1	0.4012
0.0	1.9397	900	1.8822	0.8934	0.8431	0.7982	0.8934	1	0.3976

Framework versions

• Transformers 4.42.3
• Pytorch 2.1.2
• Datasets 2.20.0
• Tokenizers 0.19.1