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metadata
license: apache-2.0
base_model: microsoft/swinv2-small-patch4-window8-256
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swinv2-small-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: 0.6875

swinv2-small-patch4-window8-256-finetuned-eurosat

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

  • Loss: 1.2717
  • Accuracy: 0.6875

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: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 4 0.6579 0.6339
No log 2.0 8 0.7129 0.5
0.6364 3.0 12 0.6774 0.5982
0.6364 4.0 16 0.6584 0.6786
0.3486 5.0 20 0.6864 0.6786
0.3486 6.0 24 0.8473 0.6429
0.3486 7.0 28 0.9735 0.6339
0.1224 8.0 32 0.8121 0.6964
0.1224 9.0 36 1.2379 0.6429
0.0424 10.0 40 1.1585 0.6875
0.0424 11.0 44 1.5274 0.6161
0.0424 12.0 48 1.1415 0.6607
0.0353 13.0 52 1.4422 0.6518
0.0353 14.0 56 1.6677 0.625
0.0141 15.0 60 1.1960 0.6696
0.0141 16.0 64 1.5515 0.625
0.0141 17.0 68 1.7990 0.6161
0.0135 18.0 72 1.4437 0.6607
0.0135 19.0 76 1.2816 0.7054
0.0073 20.0 80 1.2717 0.6875

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.2.2+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2