jayanta / google-vit-base-patch16-224-face

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Total runs: 26
24-hour runs: 0
7-day runs: 5
30-day runs: 13
Model's Last Updated: January 16 2023
image-classification

Introduction of google-vit-base-patch16-224-face

Model Details of google-vit-base-patch16-224-face

google-vit-base-patch16-224-face

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

  • Loss: 1.4531
  • Accuracy: 0.7249
  • Precision: 0.7172
  • Recall: 0.7249
  • F1: 0.7196
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: 0.00012
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 8
Training results
Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
0.8514 1.0 290 0.8464 0.7048 0.7035 0.7048 0.6909
0.7202 2.0 580 0.7791 0.7283 0.7297 0.7283 0.7111
0.5455 3.0 870 0.7950 0.7285 0.7174 0.7285 0.7171
0.334 4.0 1160 0.8948 0.7155 0.7152 0.7155 0.7145
0.1644 5.0 1450 1.0820 0.7239 0.7189 0.7239 0.7194
0.0482 6.0 1740 1.2792 0.7204 0.7144 0.7204 0.7160
0.0236 7.0 2030 1.4162 0.7279 0.7195 0.7279 0.7209
0.0049 8.0 2320 1.4531 0.7249 0.7172 0.7249 0.7196
Framework versions
  • Transformers 4.24.0.dev0
  • Pytorch 1.11.0+cu102
  • Datasets 2.6.1
  • Tokenizers 0.13.1

Runs of jayanta google-vit-base-patch16-224-face on huggingface.co

26
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3-day runs
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