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End of training
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metadata
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-fish
    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: 0.9411764705882353

swin-tiny-patch4-window7-224-finetuned-fish

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

  • Loss: 0.4264
  • Accuracy: 0.9412

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: 75

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.8 1 1.8035 0.2941
No log 1.6 2 1.7861 0.2941
No log 2.4 3 1.7554 0.2941
No log 4.0 5 1.6954 0.3529
No log 4.8 6 1.6780 0.4118
No log 5.6 7 1.6536 0.4118
No log 6.4 8 1.6222 0.4118
1.6467 8.0 10 1.4682 0.5294
1.6467 8.8 11 1.3261 0.5294
1.6467 9.6 12 1.1888 0.5294
1.6467 10.4 13 1.0433 0.5294
1.6467 12.0 15 0.8212 0.5882
1.6467 12.8 16 0.7240 0.7059
1.6467 13.6 17 0.6390 0.8235
1.6467 14.4 18 0.5594 0.8824
0.782 16.0 20 0.4647 0.8235
0.782 16.8 21 0.4264 0.9412
0.782 17.6 22 0.3983 0.9412
0.782 18.4 23 0.3760 0.9412
0.782 20.0 25 0.3751 0.8824
0.782 20.8 26 0.3553 0.8824
0.782 21.6 27 0.3161 0.8824
0.782 22.4 28 0.2706 0.9412
0.3228 24.0 30 0.2100 0.9412
0.3228 24.8 31 0.1885 0.9412
0.3228 25.6 32 0.1727 0.9412
0.3228 26.4 33 0.1818 0.9412
0.3228 28.0 35 0.1959 0.8824
0.3228 28.8 36 0.1889 0.9412
0.3228 29.6 37 0.1995 0.8824
0.3228 30.4 38 0.2093 0.8824
0.2375 32.0 40 0.1869 0.9412
0.2375 32.8 41 0.1648 0.9412
0.2375 33.6 42 0.1576 0.9412
0.2375 34.4 43 0.1709 0.9412
0.2375 36.0 45 0.1717 0.9412
0.2375 36.8 46 0.1783 0.9412
0.2375 37.6 47 0.1993 0.8824
0.2375 38.4 48 0.2085 0.8824
0.1897 40.0 50 0.2028 0.8824
0.1897 40.8 51 0.1704 0.9412
0.1897 41.6 52 0.1520 0.9412
0.1897 42.4 53 0.1325 0.9412
0.1897 44.0 55 0.1451 0.9412
0.1897 44.8 56 0.1664 0.9412
0.1897 45.6 57 0.1927 0.8824
0.1897 46.4 58 0.2202 0.8824
0.1676 48.0 60 0.2569 0.8824
0.1676 48.8 61 0.2748 0.8824
0.1676 49.6 62 0.2612 0.8824
0.1676 50.4 63 0.2414 0.8824
0.1676 52.0 65 0.1842 0.8824
0.1676 52.8 66 0.1597 0.8824
0.1676 53.6 67 0.1447 0.8824
0.1676 54.4 68 0.1359 0.9412
0.1452 56.0 70 0.1367 0.9412
0.1452 56.8 71 0.1402 0.9412
0.1452 57.6 72 0.1462 0.8824
0.1452 58.4 73 0.1515 0.8824
0.1452 60.0 75 0.1585 0.8824

Framework versions

  • Transformers 4.40.0.dev0
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.15.0