jayanta / google-vit-base-patch16-224-cartoon-emotion-detection

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Total runs: 21
24-hour runs: 0
7-day runs: -2
30-day runs: -6
Model's Last Updated: February 02 2023
image-classification

Introduction of google-vit-base-patch16-224-cartoon-emotion-detection

Model Details of google-vit-base-patch16-224-cartoon-emotion-detection

google-vit-base-patch16-224-cartoon-emotion-detection

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: 0.3706
  • Accuracy: 0.8807
  • Precision: 0.8769
  • Recall: 0.8807
  • F1: 0.8783
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: 10
Training results
Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
No log 0.97 8 0.9902 0.5596 0.5506 0.5596 0.5360
1.242 1.97 16 0.5157 0.8165 0.8195 0.8165 0.8132
0.4438 2.97 24 0.3871 0.8440 0.8516 0.8440 0.8446
0.1768 3.97 32 0.3531 0.8624 0.8653 0.8624 0.8585
0.0661 4.97 40 0.3780 0.8716 0.8693 0.8716 0.8674
0.0661 5.97 48 0.3747 0.8624 0.8649 0.8624 0.8632
0.0375 6.97 56 0.3760 0.8991 0.8961 0.8991 0.8971
0.0362 7.97 64 0.4092 0.8716 0.8684 0.8716 0.8681
0.0322 8.97 72 0.3499 0.8899 0.8880 0.8899 0.8888
0.029 9.97 80 0.3706 0.8807 0.8769 0.8807 0.8783
Framework versions
  • Transformers 4.25.1
  • Pytorch 1.13.1+cu117
  • Datasets 2.8.0
  • Tokenizers 0.11.0

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