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metadata
tags:
  - generated_from_trainer
datasets:
  - preprocessed1024_config
metrics:
  - accuracy
  - f1
model-index:
  - name: convnext-mlo-512-breat_composition
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: preprocessed1024_config
          type: preprocessed1024_config
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value:
              accuracy: 0.5785175879396985
          - name: F1
            type: f1
            value:
              f1: 0.565251065728165

convnext-mlo-512-breat_composition

This model is a fine-tuned version of on the preprocessed1024_config dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1521
  • Accuracy: {'accuracy': 0.5785175879396985}
  • F1: {'f1': 0.565251065728165}

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
1.3433 1.0 796 1.1893 {'accuracy': 0.4566582914572864} {'f1': 0.32080438921262083}
1.1242 2.0 1592 1.0867 {'accuracy': 0.48555276381909546} {'f1': 0.4061780745199038}
1.0569 3.0 2388 1.1587 {'accuracy': 0.49120603015075376} {'f1': 0.40970823779940124}
0.9327 4.0 3184 0.9901 {'accuracy': 0.5452261306532663} {'f1': 0.4885626990630958}
0.8723 5.0 3980 0.9824 {'accuracy': 0.5728643216080402} {'f1': 0.5365052338942904}
0.7803 6.0 4776 1.0071 {'accuracy': 0.571608040201005} {'f1': 0.5246756181464156}
0.7198 7.0 5572 1.0233 {'accuracy': 0.5741206030150754} {'f1': 0.5405969058526473}
0.6589 8.0 6368 1.0902 {'accuracy': 0.5816582914572864} {'f1': 0.5421523761661359}
0.6055 9.0 7164 1.0980 {'accuracy': 0.5835427135678392} {'f1': 0.5601877104043351}
0.5722 10.0 7960 1.1521 {'accuracy': 0.5785175879396985} {'f1': 0.565251065728165}

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.12.0
  • Datasets 2.1.0
  • Tokenizers 0.12.1