---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_model_94
  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. -->

# test_model_94

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the corranm/first_vote_100_per_new2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8933
- F1 Macro: 0.0863
- F1 Micro: 0.2197
- F1 Weighted: 0.1195
- Precision Macro: 0.0630
- Precision Micro: 0.2197
- Precision Weighted: 0.0868
- Recall Macro: 0.1568
- Recall Micro: 0.2197
- Recall Weighted: 0.2197
- Accuracy: 0.2197

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | F1 Weighted | Precision Macro | Precision Micro | Precision Weighted | Recall Macro | Recall Micro | Recall Weighted | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.9541        | 0.8   | 3    | 1.9150          | 0.0426   | 0.1591   | 0.0609      | 0.0263          | 0.1591          | 0.0377             | 0.1111       | 0.1591       | 0.1591          | 0.1591   |
| 1.9037        | 1.8   | 6    | 1.8975          | 0.0848   | 0.2121   | 0.1175      | 0.0601          | 0.2121          | 0.0831             | 0.1520       | 0.2121       | 0.2121          | 0.2121   |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0