---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_classification
  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: 0.58125
---

<!-- 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. -->

# emotion_classification

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: 1.4116
- Accuracy: 0.5813

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 10   | 1.3914          | 0.5312   |
| No log        | 2.0   | 20   | 1.3253          | 0.4875   |
| No log        | 3.0   | 30   | 1.4217          | 0.4813   |
| No log        | 4.0   | 40   | 1.3711          | 0.5062   |
| No log        | 5.0   | 50   | 1.3584          | 0.5      |
| No log        | 6.0   | 60   | 1.3163          | 0.5      |
| No log        | 7.0   | 70   | 1.3824          | 0.5188   |
| No log        | 8.0   | 80   | 1.3882          | 0.525    |
| No log        | 9.0   | 90   | 1.4126          | 0.5188   |
| No log        | 10.0  | 100  | 1.3213          | 0.5625   |
| No log        | 11.0  | 110  | 1.4385          | 0.5      |
| No log        | 12.0  | 120  | 1.3729          | 0.525    |
| No log        | 13.0  | 130  | 1.4603          | 0.4938   |
| No log        | 14.0  | 140  | 1.5326          | 0.4688   |
| No log        | 15.0  | 150  | 1.3687          | 0.5563   |
| No log        | 16.0  | 160  | 1.4537          | 0.55     |
| No log        | 17.0  | 170  | 1.5377          | 0.5188   |
| No log        | 18.0  | 180  | 1.6417          | 0.4688   |
| No log        | 19.0  | 190  | 1.5260          | 0.55     |
| No log        | 20.0  | 200  | 1.6854          | 0.4938   |
| No log        | 21.0  | 210  | 1.6457          | 0.5062   |
| No log        | 22.0  | 220  | 1.5855          | 0.5125   |
| No log        | 23.0  | 230  | 1.5083          | 0.5312   |
| No log        | 24.0  | 240  | 1.5656          | 0.525    |
| No log        | 25.0  | 250  | 1.5931          | 0.5125   |
| No log        | 26.0  | 260  | 1.4351          | 0.5687   |
| No log        | 27.0  | 270  | 1.5031          | 0.525    |
| No log        | 28.0  | 280  | 1.4129          | 0.55     |
| No log        | 29.0  | 290  | 1.5323          | 0.5125   |
| No log        | 30.0  | 300  | 1.5217          | 0.5625   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3