README.md

metadata

tags:
  - generated_from_trainer
datasets:
  - preprocessed1024_config
metrics:
  - accuracy
  - f1
model-index:
  - name: vit-model
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: preprocessed1024_config
          type: preprocessed1024_config
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value:
              accuracy: 0.5615577889447236
          - name: F1
            type: f1
            value:
              f1: 0.5213901124963216

vit-model

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

• Loss: 0.9396
• Accuracy: {'accuracy': 0.5615577889447236}
• F1: {'f1': 0.5213901124963216}

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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
1.221	1.0	796	0.9396	{'accuracy': 0.5615577889447236}	{'f1': 0.5213901124963216}

Framework versions

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