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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: Swin_transformer_dent_detection
    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: 0.55
          - name: F1
            type: f1
            value: 0.55
          - name: Recall
            type: recall
            value: 0.55
          - name: Precision
            type: precision
            value: 0.55

Swin_transformer_dent_detection

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6854
  • Accuracy: 0.55
  • F1: 0.55
  • Recall: 0.55
  • Precision: 0.55

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: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Recall Precision
No log 0.8 1 0.6854 0.55 0.55 0.55 0.55
No log 1.8 2 0.9226 0.55 0.55 0.55 0.55
No log 2.8 3 0.8610 0.55 0.55 0.55 0.55

Framework versions

  • Transformers 4.23.1
  • Pytorch 1.13.0
  • Datasets 2.6.1
  • Tokenizers 0.13.1