transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-Visual-Emotional
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5625
---

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# vit-base-patch16-224-finetuned-Visual-Emotional

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3141
- Accuracy: 0.5625

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 32

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.87  | 5    | 2.1419          | 0.15     |
| 2.1722        | 1.91  | 11   | 2.0381          | 0.1625   |
| 2.1722        | 2.96  | 17   | 1.8729          | 0.3      |
| 1.8696        | 4.0   | 23   | 1.6683          | 0.3625   |
| 1.8696        | 4.87  | 28   | 1.5172          | 0.4      |
| 1.4531        | 5.91  | 34   | 1.3960          | 0.4625   |
| 1.1483        | 6.96  | 40   | 1.3788          | 0.45     |
| 1.1483        | 8.0   | 46   | 1.3186          | 0.5125   |
| 0.955         | 8.87  | 51   | 1.2741          | 0.475    |
| 0.955         | 9.91  | 57   | 1.2990          | 0.5      |
| 0.7894        | 10.96 | 63   | 1.2462          | 0.475    |
| 0.7894        | 12.0  | 69   | 1.3090          | 0.5375   |
| 0.6769        | 12.87 | 74   | 1.2809          | 0.5125   |
| 0.5958        | 13.91 | 80   | 1.3020          | 0.525    |
| 0.5958        | 14.96 | 86   | 1.3032          | 0.5      |
| 0.5179        | 16.0  | 92   | 1.2624          | 0.5375   |
| 0.5179        | 16.87 | 97   | 1.2776          | 0.525    |
| 0.4808        | 17.91 | 103  | 1.2705          | 0.525    |
| 0.4808        | 18.96 | 109  | 1.2792          | 0.5125   |
| 0.4025        | 20.0  | 115  | 1.2923          | 0.5375   |
| 0.3908        | 20.87 | 120  | 1.3156          | 0.525    |
| 0.3908        | 21.91 | 126  | 1.3290          | 0.5375   |
| 0.3384        | 22.96 | 132  | 1.3141          | 0.5625   |
| 0.3384        | 24.0  | 138  | 1.3253          | 0.55     |
| 0.3428        | 24.87 | 143  | 1.3502          | 0.5375   |
| 0.3428        | 25.91 | 149  | 1.3498          | 0.525    |
| 0.3236        | 26.96 | 155  | 1.3450          | 0.525    |
| 0.2951        | 27.83 | 160  | 1.3425          | 0.5375   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
