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metadata
library_name: transformers
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: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.55625

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: 1.2851
  • Accuracy: 0.5563

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
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.9706 1.0 20 1.9258 0.35
1.672 2.0 40 1.7025 0.4625
1.4489 3.0 60 1.5581 0.4313
1.2031 4.0 80 1.4534 0.5
0.9503 5.0 100 1.3794 0.5
0.758 6.0 120 1.3283 0.5312
0.6021 7.0 140 1.3007 0.5125
0.4784 8.0 160 1.2851 0.5563
0.3682 9.0 180 1.2815 0.525
0.3117 10.0 200 1.3074 0.5125
0.2753 11.0 220 1.2945 0.525
0.2585 12.0 240 1.2903 0.5375
0.2483 13.0 260 1.2903 0.5437
0.245 14.0 280 1.2927 0.5375
0.2459 15.0 300 1.2925 0.5375

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1