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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
