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End of training
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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: image_classification
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: en-US
          split: train
          args: en-US
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.55

image_classification

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2586
  • Accuracy: 0.55

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 40 1.8677 0.3688
No log 2.0 80 1.5622 0.3625
No log 3.0 120 1.4344 0.5375
No log 4.0 160 1.2909 0.5
No log 5.0 200 1.2146 0.6
No log 6.0 240 1.2457 0.55
No log 7.0 280 1.2429 0.5563
No log 8.0 320 1.2015 0.5375
No log 9.0 360 1.2393 0.5188
No log 10.0 400 1.1908 0.5687
No log 11.0 440 1.1580 0.6188
No log 12.0 480 1.1608 0.575
1.0532 13.0 520 1.2468 0.5687
1.0532 14.0 560 1.2747 0.5188
1.0532 15.0 600 1.3293 0.525
1.0532 16.0 640 1.3720 0.525
1.0532 17.0 680 1.4374 0.5125
1.0532 18.0 720 1.3092 0.5687
1.0532 19.0 760 1.4143 0.5437
1.0532 20.0 800 1.5023 0.4938
1.0532 21.0 840 1.4033 0.575
1.0532 22.0 880 1.4476 0.5437
1.0532 23.0 920 1.3089 0.5813
1.0532 24.0 960 1.3866 0.5813
0.3016 25.0 1000 1.3748 0.5875
0.3016 26.0 1040 1.5846 0.5312
0.3016 27.0 1080 1.3451 0.5875
0.3016 28.0 1120 1.5289 0.5062
0.3016 29.0 1160 1.6067 0.5125
0.3016 30.0 1200 1.5002 0.5375
0.3016 31.0 1240 1.5404 0.55
0.3016 32.0 1280 1.5542 0.5563
0.3016 33.0 1320 1.4320 0.6062
0.3016 34.0 1360 1.6465 0.5312
0.3016 35.0 1400 1.7259 0.5062
0.3016 36.0 1440 1.5655 0.5687
0.3016 37.0 1480 1.4517 0.6188
0.1764 38.0 1520 1.5884 0.575
0.1764 39.0 1560 1.4692 0.5813
0.1764 40.0 1600 1.5062 0.6125
0.1764 41.0 1640 1.5122 0.6
0.1764 42.0 1680 1.5859 0.6
0.1764 43.0 1720 1.6816 0.525
0.1764 44.0 1760 1.5594 0.6062
0.1764 45.0 1800 1.7011 0.5375
0.1764 46.0 1840 1.5676 0.575
0.1764 47.0 1880 1.5260 0.6
0.1764 48.0 1920 1.5711 0.575
0.1764 49.0 1960 1.7095 0.5563
0.1256 50.0 2000 1.7625 0.5188

Framework versions

  • Transformers 4.33.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3