---
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