metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
model-index:
  - name: convnext-tiny-224-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: 0.8235294117647058
          - name: F1
            type: f1
            value: 0.7272727272727272

convnext-tiny-224-finetuned-brs

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

Loss: 0.8667
Accuracy: 0.8235
F1: 0.7273
Precision (ppv): 0.8
Recall (sensitivity): 0.6667
Specificity: 0.9091
Npv: 0.8333
Auc: 0.7879

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
0.6766	6.25	100	0.7002	0.4706	0.5263	0.3846	0.8333	0.2727	0.75	0.5530
0.6408	12.49	200	0.6770	0.6471	0.5714	0.5	0.6667	0.6364	0.7778	0.6515
0.464	18.74	300	0.6624	0.5882	0.5882	0.4545	0.8333	0.4545	0.8333	0.6439
0.4295	24.98	400	0.6938	0.5294	0.5	0.4	0.6667	0.4545	0.7143	0.5606
0.3952	31.25	500	0.5974	0.7059	0.6154	0.5714	0.6667	0.7273	0.8	0.6970
0.1082	37.49	600	0.6163	0.6471	0.5	0.5	0.5	0.7273	0.7273	0.6136
0.1997	43.74	700	0.6155	0.7059	0.6154	0.5714	0.6667	0.7273	0.8	0.6970
0.1267	49.98	800	0.9063	0.6471	0.5714	0.5	0.6667	0.6364	0.7778	0.6515
0.1178	56.25	900	0.8672	0.7059	0.6667	0.5556	0.8333	0.6364	0.875	0.7348
0.2008	62.49	1000	0.7049	0.8235	0.7692	0.7143	0.8333	0.8182	0.9	0.8258
0.0996	68.74	1100	0.4510	0.8235	0.7692	0.7143	0.8333	0.8182	0.9	0.8258
0.0115	74.98	1200	0.7561	0.8235	0.7692	0.7143	0.8333	0.8182	0.9	0.8258
0.0177	81.25	1300	1.0400	0.7059	0.6667	0.5556	0.8333	0.6364	0.875	0.7348
0.0261	87.49	1400	0.9139	0.8235	0.7692	0.7143	0.8333	0.8182	0.9	0.8258
0.028	93.74	1500	0.7367	0.7647	0.7143	0.625	0.8333	0.7273	0.8889	0.7803
0.0056	99.98	1600	0.8667	0.8235	0.7273	0.8	0.6667	0.9091	0.8333	0.7879

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1