---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_recognition
  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: 0.6125
---

<!-- 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_recognition

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: 1.2014
- Accuracy: 0.6125

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0842        | 1.0   | 10   | 2.0668          | 0.175    |
| 2.039         | 2.0   | 20   | 2.0070          | 0.2875   |
| 1.9285        | 3.0   | 30   | 1.8789          | 0.4062   |
| 1.7699        | 4.0   | 40   | 1.6942          | 0.425    |
| 1.6135        | 5.0   | 50   | 1.5758          | 0.4313   |
| 1.5056        | 6.0   | 60   | 1.4884          | 0.55     |
| 1.3896        | 7.0   | 70   | 1.3999          | 0.5437   |
| 1.2804        | 8.0   | 80   | 1.3563          | 0.5437   |
| 1.2043        | 9.0   | 90   | 1.3244          | 0.55     |
| 1.1231        | 10.0  | 100  | 1.2775          | 0.6062   |
| 1.0652        | 11.0  | 110  | 1.2567          | 0.575    |
| 1.0005        | 12.0  | 120  | 1.2833          | 0.5563   |
| 0.9878        | 13.0  | 130  | 1.2277          | 0.5687   |
| 0.9714        | 14.0  | 140  | 1.2557          | 0.5563   |
| 0.9057        | 15.0  | 150  | 1.2187          | 0.6125   |
| 0.8854        | 16.0  | 160  | 1.2612          | 0.5437   |
| 0.8478        | 17.0  | 170  | 1.2450          | 0.5437   |
| 0.8601        | 18.0  | 180  | 1.2456          | 0.5375   |
| 0.8498        | 19.0  | 190  | 1.2413          | 0.5875   |
| 0.8775        | 20.0  | 200  | 1.1928          | 0.6      |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1