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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: Brain_Tumor_Detector_swin
    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: 0.9981308411214953
          - name: F1
            type: f1
            value: 0.9985111662531018
          - name: Recall
            type: recall
            value: 0.9990069513406157
          - name: Precision
            type: precision
            value: 0.998015873015873

Brain_Tumor_Detector_swin

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: 0.0054
  • Accuracy: 0.9981
  • F1: 0.9985
  • Recall: 0.9990
  • Precision: 0.9980

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
0.079 1.0 113 0.0283 0.9882 0.9906 0.9930 0.9881
0.0575 2.0 226 0.0121 0.9956 0.9965 0.9950 0.9980
0.0312 3.0 339 0.0054 0.9981 0.9985 0.9990 0.9980

Framework versions

  • Transformers 4.23.1
  • Pytorch 1.12.1
  • Datasets 2.6.1
  • Tokenizers 0.13.1