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metadata
license: apache-2.0
base_model: DouglasBraga/swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: >-
      swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000-finetuned-leukemia-1000
    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: 1

swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000-finetuned-leukemia-1000

This model is a fine-tuned version of DouglasBraga/swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0056
  • Accuracy: 1.0

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: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.5471 0.9825 14 0.1240 0.955
0.1792 1.9649 28 0.0493 0.985
0.0936 2.9474 42 0.1210 0.965
0.0907 4.0 57 0.0056 1.0
0.0441 4.9825 71 0.0165 0.995
0.0341 5.9649 85 0.0059 0.995
0.0406 6.9474 99 0.0018 1.0
0.013 8.0 114 0.0200 0.995
0.0342 8.9825 128 0.0030 1.0
0.0246 9.8246 140 0.0026 1.0

Framework versions

  • Transformers 4.40.0.dev0
  • Pytorch 2.2.2+cpu
  • Datasets 2.19.0
  • Tokenizers 0.15.2