jayanta / vit-base-patch16-224-FV2-finetuned-memes

huggingface.co
Total runs: 8
24-hour runs: 0
7-day runs: 1
30-day runs: 1
Model's Last Updated: October 21 2022
image-classification

Introduction of vit-base-patch16-224-FV2-finetuned-memes

Model Details of vit-base-patch16-224-FV2-finetuned-memes

vit-base-patch16-224-FV2-finetuned-memes

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: 0.5458
  • Accuracy: 0.8648
  • Precision: 0.8651
  • Recall: 0.8648
  • F1: 0.8646
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: 20
Training results
Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
0.994 0.99 20 0.7937 0.7257 0.7148 0.7257 0.7025
0.509 1.99 40 0.4634 0.8346 0.8461 0.8346 0.8303
0.2698 2.99 60 0.3851 0.8594 0.8619 0.8594 0.8586
0.1381 3.99 80 0.4186 0.8624 0.8716 0.8624 0.8634
0.0899 4.99 100 0.4038 0.8586 0.8624 0.8586 0.8594
0.0708 5.99 120 0.4170 0.8563 0.8612 0.8563 0.8580
0.0629 6.99 140 0.4414 0.8594 0.8599 0.8594 0.8585
0.0554 7.99 160 0.4617 0.8539 0.8563 0.8539 0.8550
0.0582 8.99 180 0.4712 0.8648 0.8667 0.8648 0.8651
0.0582 9.99 200 0.4753 0.8632 0.8647 0.8632 0.8636
0.0535 10.99 220 0.4653 0.8694 0.8690 0.8694 0.8684
0.0516 11.99 240 0.4937 0.8679 0.8692 0.8679 0.8681
0.0478 12.99 260 0.5109 0.8725 0.8741 0.8725 0.8718
0.0484 13.99 280 0.5144 0.8640 0.8660 0.8640 0.8647
0.0472 14.99 300 0.5249 0.8679 0.8688 0.8679 0.8678
0.043 15.99 320 0.5324 0.8709 0.8711 0.8709 0.8704
0.0473 16.99 340 0.5352 0.8648 0.8660 0.8648 0.8647
0.0502 17.99 360 0.5389 0.8694 0.8692 0.8694 0.8687
0.0489 18.99 380 0.5564 0.8648 0.8666 0.8648 0.8651
0.04 19.99 400 0.5458 0.8648 0.8651 0.8648 0.8646
Framework versions
  • Transformers 4.24.0.dev0
  • Pytorch 1.11.0+cu102
  • Datasets 2.6.1.dev0
  • Tokenizers 0.13.1

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