---
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: 0.65625
    - name: Precision
      type: precision
      value: 0.6864745278875714
    - name: F1
      type: f1
      value: 0.6531282051282051
---

<!-- 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: 1.0743
- Accuracy: 0.6562
- Precision: 0.6865
- F1: 0.6531

## 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: 100
- num_epochs: 300

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 2.0912        | 1.0   | 10   | 2.0884          | 0.0938   | 0.0557    | 0.0679 |
| 2.086         | 2.0   | 20   | 2.0835          | 0.1062   | 0.1076    | 0.0825 |
| 2.0724        | 3.0   | 30   | 2.0743          | 0.15     | 0.1595    | 0.1235 |
| 2.0575        | 4.0   | 40   | 2.0614          | 0.1625   | 0.1451    | 0.1291 |
| 2.0375        | 5.0   | 50   | 2.0399          | 0.2125   | 0.2375    | 0.1880 |
| 1.9952        | 6.0   | 60   | 1.9954          | 0.2875   | 0.4219    | 0.2692 |
| 1.9309        | 7.0   | 70   | 1.9096          | 0.3312   | 0.4116    | 0.3141 |
| 1.8219        | 8.0   | 80   | 1.7690          | 0.375    | 0.4091    | 0.3375 |
| 1.6907        | 9.0   | 90   | 1.6323          | 0.4      | 0.4548    | 0.3595 |
| 1.5937        | 10.0  | 100  | 1.5317          | 0.4437   | 0.4015    | 0.4174 |
| 1.5157        | 11.0  | 110  | 1.4620          | 0.5312   | 0.4945    | 0.5078 |
| 1.4458        | 12.0  | 120  | 1.4050          | 0.5125   | 0.4734    | 0.4880 |
| 1.3712        | 13.0  | 130  | 1.3719          | 0.5375   | 0.5776    | 0.5236 |
| 1.3043        | 14.0  | 140  | 1.3033          | 0.5687   | 0.6482    | 0.5547 |
| 1.2424        | 15.0  | 150  | 1.2497          | 0.5813   | 0.5970    | 0.5619 |
| 1.2369        | 16.0  | 160  | 1.2423          | 0.5375   | 0.4994    | 0.5061 |
| 1.1596        | 17.0  | 170  | 1.2109          | 0.5563   | 0.5086    | 0.5216 |
| 1.1252        | 18.0  | 180  | 1.1889          | 0.5813   | 0.5772    | 0.5622 |
| 1.0746        | 19.0  | 190  | 1.1752          | 0.5625   | 0.5843    | 0.5631 |
| 1.0496        | 20.0  | 200  | 1.1402          | 0.6062   | 0.5995    | 0.5911 |
| 0.9874        | 21.0  | 210  | 1.1470          | 0.5875   | 0.5897    | 0.5720 |
| 0.9423        | 22.0  | 220  | 1.1294          | 0.6188   | 0.6174    | 0.6072 |
| 0.8842        | 23.0  | 230  | 1.1335          | 0.6      | 0.6216    | 0.6004 |
| 0.8817        | 24.0  | 240  | 1.1002          | 0.6      | 0.6078    | 0.5970 |
| 0.8365        | 25.0  | 250  | 1.1237          | 0.625    | 0.6392    | 0.6209 |
| 0.7965        | 26.0  | 260  | 1.1781          | 0.55     | 0.5888    | 0.5419 |
| 0.7829        | 27.0  | 270  | 1.1278          | 0.6      | 0.6219    | 0.5947 |
| 0.7269        | 28.0  | 280  | 1.1144          | 0.6      | 0.6386    | 0.5937 |
| 0.7158        | 29.0  | 290  | 1.1245          | 0.6125   | 0.6524    | 0.5939 |
| 0.7178        | 30.0  | 300  | 1.0692          | 0.6188   | 0.6344    | 0.6159 |
| 0.6704        | 31.0  | 310  | 1.0568          | 0.65     | 0.6724    | 0.6514 |
| 0.6371        | 32.0  | 320  | 1.0411          | 0.65     | 0.6529    | 0.6465 |
| 0.6317        | 33.0  | 330  | 1.1018          | 0.6438   | 0.6732    | 0.6416 |
| 0.5625        | 34.0  | 340  | 1.0743          | 0.6562   | 0.6865    | 0.6531 |
| 0.5717        | 35.0  | 350  | 1.1658          | 0.6062   | 0.6636    | 0.6094 |
| 0.5807        | 36.0  | 360  | 1.1473          | 0.625    | 0.6654    | 0.6161 |
| 0.5269        | 37.0  | 370  | 1.1367          | 0.6188   | 0.6317    | 0.6150 |
| 0.5284        | 38.0  | 380  | 1.0724          | 0.6438   | 0.6625    | 0.6449 |
| 0.5715        | 39.0  | 390  | 1.1805          | 0.575    | 0.6076    | 0.5711 |
| 0.486         | 40.0  | 400  | 1.1676          | 0.5938   | 0.6379    | 0.5892 |
| 0.4581        | 41.0  | 410  | 1.1633          | 0.6312   | 0.6583    | 0.6298 |
| 0.4364        | 42.0  | 420  | 1.1371          | 0.6312   | 0.6353    | 0.6255 |
| 0.4117        | 43.0  | 430  | 1.2004          | 0.625    | 0.6748    | 0.6086 |
| 0.4433        | 44.0  | 440  | 1.1082          | 0.625    | 0.6322    | 0.6232 |
| 0.4031        | 45.0  | 450  | 1.2251          | 0.5875   | 0.6395    | 0.5944 |
| 0.4205        | 46.0  | 460  | 1.2513          | 0.5938   | 0.6196    | 0.5934 |
| 0.3524        | 47.0  | 470  | 1.1704          | 0.6125   | 0.6303    | 0.6147 |
| 0.4094        | 48.0  | 480  | 1.1930          | 0.5875   | 0.6071    | 0.5892 |
| 0.369         | 49.0  | 490  | 1.1970          | 0.6188   | 0.6509    | 0.6171 |
| 0.3666        | 50.0  | 500  | 1.2280          | 0.6      | 0.6171    | 0.5971 |
| 0.4054        | 51.0  | 510  | 1.2725          | 0.5625   | 0.5807    | 0.5599 |
| 0.4247        | 52.0  | 520  | 1.2380          | 0.6188   | 0.6385    | 0.6128 |
| 0.3791        | 53.0  | 530  | 1.2402          | 0.5813   | 0.6153    | 0.5806 |
| 0.3241        | 54.0  | 540  | 1.2491          | 0.5687   | 0.5817    | 0.5676 |
| 0.3268        | 55.0  | 550  | 1.2575          | 0.5938   | 0.6058    | 0.5956 |
| 0.3419        | 56.0  | 560  | 1.3199          | 0.6      | 0.6160    | 0.5930 |
| 0.3657        | 57.0  | 570  | 1.2408          | 0.6188   | 0.6441    | 0.6207 |
| 0.3327        | 58.0  | 580  | 1.2430          | 0.6125   | 0.6200    | 0.6107 |
| 0.3126        | 59.0  | 590  | 1.3995          | 0.5312   | 0.5619    | 0.5158 |


### Framework versions

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