
<!-- 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. -->

# vet-sm

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8296
- Accuracy: 0.7440

## 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: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3577        | 1.0   | 375  | 1.3559          | 0.5333   |
| 1.1359        | 2.0   | 750  | 1.0537          | 0.6386   |
| 0.727         | 3.0   | 1125 | 0.8715          | 0.7156   |
| 0.3493        | 4.0   | 1500 | 0.8288          | 0.7355   |
| 0.1978        | 5.0   | 1875 | 0.8296          | 0.7440   |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.21.0

neutral calm happy sad angry fearful disgust surprised