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# image_emotion_classification_project_4

This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9052
- Accuracy: 0.5188

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: reduce_lr_on_plateau
- lr_scheduler_warmup_steps: 50
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6977        | 1.0   | 640  | 1.5713          | 0.325    |
| 1.7006        | 2.0   | 1280 | 1.4543          | 0.4562   |
| 1.6725        | 3.0   | 1920 | 1.6124          | 0.4625   |
| 1.2312        | 4.0   | 2560 | 1.6711          | 0.5      |
| 0.6097        | 5.0   | 3200 | 1.8838          | 0.5312   |
| 1.264         | 6.0   | 3840 | 2.0933          | 0.4875   |
| 2.4064        | 7.0   | 4480 | 2.0628          | 0.5188   |
| 2.0741        | 8.0   | 5120 | 2.6505          | 0.4625   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3

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