VANBase-finetuned-brs-finetuned-brs

This model is a fine-tuned version of Visual-Attention-Network/van-base on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.7056
• Accuracy: 0.5882
• F1: 0.6957
• Precision (ppv): 0.6154
• Recall (sensitivity): 0.8
• Specificity: 0.2857
• Npv: 0.5
• Auc: 0.5429

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: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision (ppv)	Recall (sensitivity)	Specificity	Npv	Auc
0.6589	6.25	100	0.6655	0.5882	0.6316	0.6667	0.6	0.5714	0.5	0.5857
0.6262	12.49	200	0.6917	0.5294	0.6364	0.5833	0.7	0.2857	0.4	0.4929
0.4706	18.74	300	0.6776	0.5882	0.6957	0.6154	0.8	0.2857	0.5	0.5429
0.5202	24.98	400	0.7018	0.5294	0.6	0.6	0.6	0.4286	0.4286	0.5143
0.4628	31.25	500	0.6903	0.6471	0.75	0.6429	0.9	0.2857	0.6667	0.5929
0.3525	37.49	600	0.7241	0.5294	0.6667	0.5714	0.8	0.1429	0.3333	0.4714
0.2877	43.74	700	0.8262	0.5882	0.7407	0.5882	1.0	0.0	nan	0.5
0.2921	49.98	800	0.8058	0.4706	0.64	0.5333	0.8	0.0	0.0	0.4
0.3834	56.25	900	0.7864	0.5882	0.7407	0.5882	1.0	0.0	nan	0.5
0.2267	62.49	1000	0.5520	0.7647	0.8182	0.75	0.9	0.5714	0.8	0.7357
0.3798	68.74	1100	0.8722	0.4706	0.64	0.5333	0.8	0.0	0.0	0.4
0.2633	74.98	1200	0.7260	0.6471	0.7273	0.6667	0.8	0.4286	0.6	0.6143
0.3439	81.25	1300	1.0187	0.4118	0.5455	0.5	0.6	0.1429	0.2	0.3714
0.2532	87.49	1400	0.8812	0.5882	0.7407	0.5882	1.0	0.0	nan	0.5
0.0841	93.74	1500	0.8717	0.5294	0.6923	0.5625	0.9	0.0	0.0	0.45
0.3409	99.98	1600	0.7056	0.5882	0.6957	0.6154	0.8	0.2857	0.5	0.5429

Framework versions

• Transformers 4.23.1
• Pytorch 1.12.1+cu113
• Datasets 2.6.1
• Tokenizers 0.13.1