---
library_name: transformers
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: 0.56875
---

<!-- 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: 1.2493
- Accuracy: 0.5687

## 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: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0679        | 1.0   | 10   | 2.0574          | 0.175    |
| 2.0366        | 2.0   | 20   | 2.0083          | 0.2812   |
| 1.9469        | 3.0   | 30   | 1.9119          | 0.35     |
| 1.8166        | 4.0   | 40   | 1.7702          | 0.4125   |
| 1.6821        | 5.0   | 50   | 1.6176          | 0.45     |
| 1.5587        | 6.0   | 60   | 1.5747          | 0.425    |
| 1.4703        | 7.0   | 70   | 1.4444          | 0.5375   |
| 1.4032        | 8.0   | 80   | 1.4226          | 0.5312   |
| 1.3367        | 9.0   | 90   | 1.3937          | 0.5188   |
| 1.2889        | 10.0  | 100  | 1.3186          | 0.5375   |
| 1.2136        | 11.0  | 110  | 1.3313          | 0.55     |
| 1.1745        | 12.0  | 120  | 1.3027          | 0.5312   |
| 1.1477        | 13.0  | 130  | 1.3004          | 0.5375   |
| 1.1414        | 14.0  | 140  | 1.2442          | 0.55     |
| 1.1202        | 15.0  | 150  | 1.2957          | 0.5062   |
| 1.0923        | 16.0  | 160  | 1.3045          | 0.5125   |
| 1.0765        | 17.0  | 170  | 1.2533          | 0.5563   |
| 1.0678        | 18.0  | 180  | 1.2392          | 0.5437   |
| 1.0837        | 19.0  | 190  | 1.2750          | 0.5375   |
| 1.0562        | 20.0  | 200  | 1.2275          | 0.5625   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1