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vit-base-patch16-224-FV-20epochs-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: 0.6532
- Accuracy: 0.8632
- Precision: 0.8617
- Recall: 0.8632
- F1: 0.8621
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 |
|---|---|---|---|---|---|---|---|
| 1.1709 | 0.99 | 20 | 0.9393 | 0.6971 | 0.6896 | 0.6971 | 0.6890 |
| 0.5295 | 1.99 | 40 | 0.5024 | 0.8091 | 0.8210 | 0.8091 | 0.8133 |
| 0.2909 | 2.99 | 60 | 0.4070 | 0.8539 | 0.8529 | 0.8539 | 0.8529 |
| 0.1435 | 3.99 | 80 | 0.4136 | 0.8539 | 0.8522 | 0.8539 | 0.8522 |
| 0.0928 | 4.99 | 100 | 0.4495 | 0.8478 | 0.8548 | 0.8478 | 0.8507 |
| 0.0643 | 5.99 | 120 | 0.4897 | 0.8594 | 0.8572 | 0.8594 | 0.8573 |
| 0.061 | 6.99 | 140 | 0.5040 | 0.8423 | 0.8490 | 0.8423 | 0.8453 |
| 0.0519 | 7.99 | 160 | 0.5266 | 0.8524 | 0.8502 | 0.8524 | 0.8510 |
| 0.0546 | 8.99 | 180 | 0.5200 | 0.8586 | 0.8632 | 0.8586 | 0.8605 |
| 0.0478 | 9.99 | 200 | 0.5654 | 0.8555 | 0.8548 | 0.8555 | 0.8548 |
| 0.0509 | 10.99 | 220 | 0.5774 | 0.8609 | 0.8626 | 0.8609 | 0.8616 |
| 0.0467 | 11.99 | 240 | 0.5847 | 0.8594 | 0.8602 | 0.8594 | 0.8594 |
| 0.0468 | 12.99 | 260 | 0.5909 | 0.8601 | 0.8597 | 0.8601 | 0.8596 |
| 0.0469 | 13.99 | 280 | 0.5970 | 0.8563 | 0.8560 | 0.8563 | 0.8561 |
| 0.0438 | 14.99 | 300 | 0.6234 | 0.8594 | 0.8583 | 0.8594 | 0.8586 |
| 0.0441 | 15.99 | 320 | 0.6190 | 0.8563 | 0.8582 | 0.8563 | 0.8570 |
| 0.0431 | 16.99 | 340 | 0.6419 | 0.8570 | 0.8584 | 0.8570 | 0.8574 |
| 0.0454 | 17.99 | 360 | 0.6528 | 0.8563 | 0.8556 | 0.8563 | 0.8558 |
| 0.0417 | 18.99 | 380 | 0.6688 | 0.8578 | 0.8575 | 0.8578 | 0.8574 |
| 0.0432 | 19.99 | 400 | 0.6532 | 0.8632 | 0.8617 | 0.8632 | 0.8621 |
Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1.dev0
- Tokenizers 0.13.1
