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metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-base-cat-emotions
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: custom dataset
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6352941176470588

vit-base-cat-emotions

You can try out the model live here, and check out the GitHub repository for more details.

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

  • Loss: 1.0160
  • Accuracy: 0.6353

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: 0.0002
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.3361 3.125 100 1.0125 0.6548
0.0723 6.25 200 0.9043 0.7381
0.0321 9.375 300 0.9268 0.7143

Framework versions

  • Transformers 4.44.1
  • Pytorch 2.2.2+cu118
  • Datasets 2.20.0
  • Tokenizers 0.19.1