---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
- precision
- f1
model-index:
- name: emotion_classification
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      config: FastJobs--Visual_Emotional_Analysis
      split: train
      args: FastJobs--Visual_Emotional_Analysis
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.64375
    - name: Precision
      type: precision
      value: 0.6639732142857142
    - name: F1
      type: f1
      value: 0.640682001352849
---

<!-- 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 image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0750
- Accuracy: 0.6438
- Precision: 0.6640
- F1: 0.6407

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 50
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 2.0755        | 1.0   | 10   | 2.0787          | 0.1437   | 0.1529    | 0.1414 |
| 2.0711        | 2.0   | 20   | 2.0698          | 0.1875   | 0.1926    | 0.1832 |
| 2.0533        | 3.0   | 30   | 2.0520          | 0.2      | 0.2127    | 0.1961 |
| 2.0225        | 4.0   | 40   | 2.0173          | 0.225    | 0.2228    | 0.2054 |
| 1.9569        | 5.0   | 50   | 1.9289          | 0.2812   | 0.3345    | 0.2544 |
| 1.8501        | 6.0   | 60   | 1.7792          | 0.3688   | 0.4904    | 0.3225 |
| 1.7072        | 7.0   | 70   | 1.6236          | 0.4313   | 0.4131    | 0.3883 |
| 1.6065        | 8.0   | 80   | 1.5276          | 0.45     | 0.4533    | 0.3920 |
| 1.539         | 9.0   | 90   | 1.4747          | 0.4938   | 0.4748    | 0.4563 |
| 1.5086        | 10.0  | 100  | 1.4393          | 0.4938   | 0.4557    | 0.4466 |
| 1.4479        | 11.0  | 110  | 1.3893          | 0.5188   | 0.4563    | 0.4696 |
| 1.3683        | 12.0  | 120  | 1.3534          | 0.5437   | 0.5081    | 0.5149 |
| 1.3288        | 13.0  | 130  | 1.3392          | 0.5563   | 0.5569    | 0.5323 |
| 1.2514        | 14.0  | 140  | 1.2723          | 0.5625   | 0.5467    | 0.5246 |
| 1.2116        | 15.0  | 150  | 1.2526          | 0.5875   | 0.5554    | 0.5601 |
| 1.1824        | 16.0  | 160  | 1.2047          | 0.5938   | 0.6100    | 0.5697 |
| 1.1323        | 17.0  | 170  | 1.1950          | 0.5813   | 0.5331    | 0.5472 |
| 1.0782        | 18.0  | 180  | 1.1802          | 0.5875   | 0.5911    | 0.5807 |
| 1.0304        | 19.0  | 190  | 1.1534          | 0.6125   | 0.6133    | 0.6012 |
| 0.982         | 20.0  | 200  | 1.1302          | 0.6      | 0.5923    | 0.5806 |
| 0.9309        | 21.0  | 210  | 1.1849          | 0.5938   | 0.6157    | 0.5723 |
| 0.9205        | 22.0  | 220  | 1.1483          | 0.6      | 0.6137    | 0.5882 |
| 0.8275        | 23.0  | 230  | 1.1332          | 0.5938   | 0.6192    | 0.5894 |
| 0.8472        | 24.0  | 240  | 1.1195          | 0.625    | 0.6444    | 0.6242 |
| 0.7974        | 25.0  | 250  | 1.1444          | 0.6062   | 0.6277    | 0.6035 |
| 0.7532        | 26.0  | 260  | 1.1312          | 0.5875   | 0.6036    | 0.5832 |
| 0.7596        | 27.0  | 270  | 1.1217          | 0.6062   | 0.6412    | 0.6098 |
| 0.6818        | 28.0  | 280  | 1.1736          | 0.5625   | 0.6180    | 0.5473 |
| 0.6484        | 29.0  | 290  | 1.1630          | 0.5563   | 0.5887    | 0.5367 |
| 0.6578        | 30.0  | 300  | 1.0750          | 0.6438   | 0.6640    | 0.6407 |
| 0.6235        | 31.0  | 310  | 1.0676          | 0.6438   | 0.6556    | 0.6422 |
| 0.5966        | 32.0  | 320  | 1.0531          | 0.6438   | 0.6421    | 0.6385 |
| 0.5819        | 33.0  | 330  | 1.1244          | 0.6188   | 0.6315    | 0.6176 |
| 0.5585        | 34.0  | 340  | 1.1466          | 0.5813   | 0.6136    | 0.5790 |
| 0.5696        | 35.0  | 350  | 1.0703          | 0.6438   | 0.6614    | 0.6481 |
| 0.5476        | 36.0  | 360  | 1.1136          | 0.6438   | 0.6764    | 0.6466 |
| 0.475         | 37.0  | 370  | 1.1122          | 0.6375   | 0.6612    | 0.6340 |
| 0.5381        | 38.0  | 380  | 1.1547          | 0.6188   | 0.6570    | 0.6122 |
| 0.5161        | 39.0  | 390  | 1.2268          | 0.5875   | 0.6161    | 0.5704 |
| 0.4528        | 40.0  | 400  | 1.1065          | 0.6188   | 0.6314    | 0.6122 |
| 0.401         | 41.0  | 410  | 1.1209          | 0.6438   | 0.6550    | 0.6440 |
| 0.4067        | 42.0  | 420  | 1.1440          | 0.6312   | 0.6345    | 0.6251 |
| 0.3831        | 43.0  | 430  | 1.1972          | 0.6188   | 0.6480    | 0.6075 |
| 0.4073        | 44.0  | 440  | 1.2422          | 0.6062   | 0.6644    | 0.6028 |
| 0.371         | 45.0  | 450  | 1.2152          | 0.5875   | 0.6087    | 0.5848 |
| 0.396         | 46.0  | 460  | 1.1972          | 0.6125   | 0.6306    | 0.6106 |
| 0.3322        | 47.0  | 470  | 1.2979          | 0.5813   | 0.6158    | 0.5811 |
| 0.3691        | 48.0  | 480  | 1.1657          | 0.625    | 0.6371    | 0.6162 |
| 0.3219        | 49.0  | 490  | 1.1786          | 0.6      | 0.6417    | 0.5997 |
| 0.3371        | 50.0  | 500  | 1.2126          | 0.6188   | 0.6396    | 0.6149 |
| 0.3781        | 51.0  | 510  | 1.2246          | 0.6      | 0.6244    | 0.5972 |
| 0.3629        | 52.0  | 520  | 1.1820          | 0.6188   | 0.6437    | 0.6122 |
| 0.3025        | 53.0  | 530  | 1.1795          | 0.6062   | 0.6326    | 0.6063 |
| 0.309         | 54.0  | 540  | 1.1647          | 0.625    | 0.6510    | 0.6252 |
| 0.2999        | 55.0  | 550  | 1.2023          | 0.6375   | 0.6449    | 0.6373 |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3