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

<!-- 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. -->

# rgai_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: 1.3077
- 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: 2e-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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0698        | 1.0   | 25   | 2.0921          | 0.1125   |
| 1.973         | 2.0   | 50   | 1.9930          | 0.1938   |
| 1.8091        | 3.0   | 75   | 1.8374          | 0.3937   |
| 1.5732        | 4.0   | 100  | 1.6804          | 0.475    |
| 1.4087        | 5.0   | 125  | 1.5660          | 0.5125   |
| 1.2653        | 6.0   | 150  | 1.4769          | 0.5375   |
| 1.1443        | 7.0   | 175  | 1.4084          | 0.55     |
| 0.9888        | 8.0   | 200  | 1.3633          | 0.5625   |
| 0.9029        | 9.0   | 225  | 1.3305          | 0.55     |
| 0.8372        | 10.0  | 250  | 1.3077          | 0.5813   |
| 0.7569        | 11.0  | 275  | 1.2983          | 0.5625   |
| 0.6886        | 12.0  | 300  | 1.2806          | 0.5687   |
| 0.6216        | 13.0  | 325  | 1.2718          | 0.5687   |
| 0.6385        | 14.0  | 350  | 1.2700          | 0.5563   |
| 0.6029        | 15.0  | 375  | 1.2693          | 0.5625   |


### Framework versions

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