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metadata
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: Boya1_RMSProp_1-e5_20Epoch_09Momentum_Beit-base-patch16_fold2
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6437837837837838

Boya1_RMSProp_1-e5_20Epoch_09Momentum_Beit-base-patch16_fold2

This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 3.1168
  • Accuracy: 0.6438

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: 16
  • eval_batch_size: 16
  • seed: 42
  • 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
1.0678 1.0 923 1.1860 0.5962
0.9795 2.0 1846 1.0466 0.6414
0.6213 3.0 2769 1.0577 0.6403
0.3941 4.0 3692 1.2437 0.6424
0.3011 5.0 4615 1.4589 0.6443
0.1999 6.0 5538 1.7644 0.63
0.039 7.0 6461 1.9747 0.64
0.0664 8.0 7384 2.2470 0.6368
0.0635 9.0 8307 2.4483 0.6451
0.0688 10.0 9230 2.6192 0.6516
0.0389 11.0 10153 2.7333 0.6470
0.0075 12.0 11076 2.8548 0.6446
0.0085 13.0 11999 2.9858 0.6416
0.0018 14.0 12922 2.9790 0.6424
0.0034 15.0 13845 3.0326 0.6443
0.009 16.0 14768 3.0570 0.6473
0.0005 17.0 15691 3.1227 0.6419
0.0 18.0 16614 3.1155 0.6449
0.0002 19.0 17537 3.1130 0.6454
0.0002 20.0 18460 3.1168 0.6438

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0
  • Datasets 2.14.6
  • Tokenizers 0.14.1