---
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: 1.2074
- 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 8
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 80   | 1.7180          | 0.3812   |
| No log        | 2.0   | 160  | 1.5309          | 0.3625   |
| No log        | 3.0   | 240  | 1.4981          | 0.4188   |
| No log        | 4.0   | 320  | 1.4135          | 0.4313   |
| No log        | 5.0   | 400  | 1.3722          | 0.4562   |
| No log        | 6.0   | 480  | 1.3234          | 0.5188   |
| 1.3335        | 7.0   | 560  | 1.2675          | 0.525    |
| 1.3335        | 8.0   | 640  | 1.3068          | 0.5125   |
| 1.3335        | 9.0   | 720  | 1.2965          | 0.5437   |
| 1.3335        | 10.0  | 800  | 1.3408          | 0.5125   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1