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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-in21k-shiba-inu-detector
    results: []

vit-base-patch16-224-in21k-shiba-inu-detector

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on dataset with 4 dog types including Shiba Inu.

It achieves the following results on the evaluation set:

  • Loss: 0.6511
  • Accuracy: 1.0

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: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.94 4 1.3875 0.1667
No log 1.94 8 1.2712 0.7833
1.4176 2.94 12 1.0972 0.9
1.4176 3.94 16 0.9365 0.95
1.0144 4.94 20 0.7836 0.9833
1.0144 5.94 24 0.6511 1.0
1.0144 6.94 28 0.5329 1.0
0.6329 7.94 32 0.4403 1.0
0.6329 8.94 36 0.3777 1.0
0.3821 9.94 40 0.3273 1.0
0.3821 10.94 44 0.2886 1.0
0.3821 11.94 48 0.2622 1.0
0.2655 12.94 52 0.2397 1.0
0.2655 13.94 56 0.2250 1.0
0.202 14.94 60 0.2152 1.0
0.202 15.94 64 0.2074 1.0
0.202 16.94 68 0.2003 1.0
0.1785 17.94 72 0.1960 1.0
0.1785 18.94 76 0.1936 1.0
0.1618 19.94 80 0.1930 1.0

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.11.0+cu113
  • Datasets 2.0.0
  • Tokenizers 0.11.6