---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: emotion_classification_v1.1
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train[:5000]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.575
    - name: Precision
      type: precision
      value: 0.6064414347689876
    - name: Recall
      type: recall
      value: 0.575
    - name: F1
      type: f1
      value: 0.5730570699748332
---

<!-- 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_v1.1

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: 1.2449
- Accuracy: 0.575
- Precision: 0.6064
- Recall: 0.575
- F1: 0.5731

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 40   | 1.8287          | 0.325    | 0.2995    | 0.325  | 0.2695 |
| No log        | 2.0   | 80   | 1.5621          | 0.475    | 0.4171    | 0.475  | 0.4104 |
| No log        | 3.0   | 120  | 1.4485          | 0.4188   | 0.3786    | 0.4188 | 0.3710 |
| No log        | 4.0   | 160  | 1.4040          | 0.4313   | 0.5179    | 0.4313 | 0.3963 |
| No log        | 5.0   | 200  | 1.3333          | 0.4938   | 0.5016    | 0.4938 | 0.4654 |
| No log        | 6.0   | 240  | 1.3076          | 0.4688   | 0.4698    | 0.4688 | 0.4437 |
| No log        | 7.0   | 280  | 1.3531          | 0.4813   | 0.5289    | 0.4813 | 0.4834 |
| No log        | 8.0   | 320  | 1.3118          | 0.4688   | 0.4606    | 0.4688 | 0.4619 |
| No log        | 9.0   | 360  | 1.3326          | 0.4938   | 0.5629    | 0.4938 | 0.4744 |
| No log        | 10.0  | 400  | 1.2693          | 0.4938   | 0.4825    | 0.4938 | 0.4777 |
| No log        | 11.0  | 440  | 1.2310          | 0.55     | 0.5747    | 0.55   | 0.5441 |
| No log        | 12.0  | 480  | 1.2673          | 0.5375   | 0.5418    | 0.5375 | 0.5316 |
| 1.0804        | 13.0  | 520  | 1.3161          | 0.5125   | 0.5321    | 0.5125 | 0.5048 |
| 1.0804        | 14.0  | 560  | 1.2517          | 0.55     | 0.5550    | 0.55   | 0.5430 |
| 1.0804        | 15.0  | 600  | 1.3344          | 0.5      | 0.5023    | 0.5    | 0.4848 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1