metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
model-index:
  - name: VANBase-finetuned-brs-finetuned-brs
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.5882352941176471
          - name: F1
            type: f1
            value: 0.6956521739130435

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