Erik W
update model card README.md
50f966a
metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: swinv2-tiny-patch4-window8-256-finetuned-eurosat
    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: 0.9825925925925926
          - name: F1
            type: f1
            value: 0.9825868474705166
          - name: Precision
            type: precision
            value: 0.9828193476192771
          - name: Recall
            type: recall
            value: 0.9825925925925926

swinv2-tiny-patch4-window8-256-finetuned-eurosat

This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0510
  • Accuracy: 0.9826
  • F1: 0.9826
  • Precision: 0.9828
  • Recall: 0.9826

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: 0.0001
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.2
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
0.4479 1.0 95 0.1592 0.9478 0.9478 0.9500 0.9478
0.3078 2.0 190 0.0914 0.9685 0.9686 0.9695 0.9685
0.2307 3.0 285 0.0603 0.9785 0.9785 0.9790 0.9785
0.227 4.0 380 0.0531 0.9811 0.9811 0.9814 0.9811
0.1674 5.0 475 0.0510 0.9826 0.9826 0.9828 0.9826

Framework versions

  • Transformers 4.22.1
  • Pytorch 1.12.1+cu113
  • Datasets 2.5.1
  • Tokenizers 0.12.1