transformers tensorboard safetensors vit image-classification generated_from_trainer base_model:google/vit-base-patch16-224-in21k license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-Arousal
  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. -->

# Image-Arousal

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 custom dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8522
- Accuracy: 0.6294

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9023        | 0.78  | 100  | 0.8522          | 0.6294   |
| 0.5376        | 1.56  | 200  | 0.8592          | 0.6686   |
| 0.2473        | 2.34  | 300  | 0.9559          | 0.6510   |
| 0.0691        | 3.12  | 400  | 1.1399          | 0.6275   |
| 0.0821        | 3.91  | 500  | 1.2060          | 0.6392   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
