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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: wmc_v2_vit_base_wm811k_cls_contra_learning_0916
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wmc_v2_vit_base_wm811k_cls_contra_learning_0916
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co./google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0661
- Accuracy: 0.9768
- Precision: 0.9627
- Recall: 0.9551
- F1: 0.9585
## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.1711 | 0.1697 | 100 | 0.6405 | 0.7559 | 0.6494 | 0.5790 | 0.5526 |
| 0.7143 | 0.3394 | 200 | 0.3116 | 0.8971 | 0.8478 | 0.7631 | 0.7576 |
| 0.584 | 0.5091 | 300 | 0.2060 | 0.9489 | 0.9137 | 0.8836 | 0.8940 |
| 0.4654 | 0.6788 | 400 | 0.1431 | 0.9603 | 0.9190 | 0.9289 | 0.9230 |
| 0.4465 | 0.8485 | 500 | 0.1176 | 0.9679 | 0.9458 | 0.9295 | 0.9373 |
| 0.3368 | 1.0182 | 600 | 0.1395 | 0.9550 | 0.9338 | 0.9244 | 0.9248 |
| 0.3741 | 1.1880 | 700 | 0.1541 | 0.9528 | 0.9287 | 0.9328 | 0.9269 |
| 0.3191 | 1.3577 | 800 | 0.1039 | 0.9697 | 0.9510 | 0.9453 | 0.9470 |
| 0.3354 | 1.5274 | 900 | 0.0952 | 0.9709 | 0.9530 | 0.9539 | 0.9529 |
| 0.3122 | 1.6971 | 1000 | 0.0799 | 0.9761 | 0.9456 | 0.9665 | 0.9556 |
| 0.295 | 1.8668 | 1100 | 0.0770 | 0.9758 | 0.9615 | 0.9534 | 0.9567 |
| 0.2993 | 2.0365 | 1200 | 0.0650 | 0.9794 | 0.9655 | 0.9597 | 0.9624 |
| 0.227 | 2.2062 | 1300 | 0.0717 | 0.9763 | 0.9598 | 0.9573 | 0.9584 |
| 0.2508 | 2.3759 | 1400 | 0.0653 | 0.9785 | 0.9605 | 0.9621 | 0.9613 |
| 0.3053 | 2.5456 | 1500 | 0.0629 | 0.9797 | 0.9623 | 0.9617 | 0.9620 |
| 0.2183 | 2.7153 | 1600 | 0.0676 | 0.9767 | 0.9597 | 0.9553 | 0.9572 |
| 0.219 | 2.8850 | 1700 | 0.0661 | 0.9768 | 0.9627 | 0.9551 | 0.9585 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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