---
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: image_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: 0.6
---

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# image_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 [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1877
- Accuracy: 0.6

## Model description

Hey everyone! This is the first model I’ve deployed :D. This emotion recognition model is a fine-tuned version of google/vit-base-patch16-224-in21k, trained on the ImageFolder dataset. As a first-timer, I’m proud that this model has achieved such accuracy. I plan to further train it to improve its accuracy. Wish me luck!

## Intended uses & limitations

I strongly suggest using an input picture with a clear indication of emotion, as I’ve found that the model can sometimes misinterpret the output. Additionally, this model seems to lack confidence in identifying emotions, as evidenced by the slightly varying scores.

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 160  | 1.5920          | 0.375    |
| No log        | 2.0   | 320  | 1.4689          | 0.4313   |
| No log        | 3.0   | 480  | 1.3699          | 0.4625   |
| 1.4989        | 4.0   | 640  | 1.2204          | 0.5813   |
| 1.4989        | 5.0   | 800  | 1.2019          | 0.5437   |
| 1.4989        | 6.0   | 960  | 1.2126          | 0.55     |
| 0.9362        | 7.0   | 1120 | 1.1846          | 0.5563   |
| 0.9362        | 8.0   | 1280 | 1.2819          | 0.5312   |
| 0.9362        | 9.0   | 1440 | 1.2583          | 0.525    |
| 0.5396        | 10.0  | 1600 | 1.1571          | 0.6      |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1