---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- f1
model-index:
- name: emotion_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: 0.64375
    - name: Precision
      type: precision
      value: 0.650616883116883
    - name: F1
      type: f1
      value: 0.6344950707077283
---

<!-- 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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1553
- Accuracy: 0.6438
- Precision: 0.6506
- F1: 0.6345

## 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: 3e-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: 150
- num_epochs: 300

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 2.0799        | 1.0   | 10   | 2.0707          | 0.1313   | 0.1740    | 0.1156 |
| 2.0811        | 2.0   | 20   | 2.0681          | 0.1437   | 0.1617    | 0.1245 |
| 2.0709        | 3.0   | 30   | 2.0640          | 0.1562   | 0.1544    | 0.1330 |
| 2.0701        | 4.0   | 40   | 2.0590          | 0.1688   | 0.1463    | 0.1431 |
| 2.0639        | 5.0   | 50   | 2.0529          | 0.1812   | 0.1676    | 0.1613 |
| 2.0499        | 6.0   | 60   | 2.0439          | 0.2      | 0.2050    | 0.1871 |
| 2.0387        | 7.0   | 70   | 2.0322          | 0.25     | 0.2679    | 0.2373 |
| 2.0235        | 8.0   | 80   | 2.0141          | 0.3312   | 0.3638    | 0.3331 |
| 1.9933        | 9.0   | 90   | 1.9883          | 0.3375   | 0.3752    | 0.3392 |
| 1.9573        | 10.0  | 100  | 1.9473          | 0.3563   | 0.3940    | 0.3535 |
| 1.912         | 11.0  | 110  | 1.8863          | 0.3875   | 0.4352    | 0.3759 |
| 1.8306        | 12.0  | 120  | 1.8102          | 0.3875   | 0.4062    | 0.3586 |
| 1.7479        | 13.0  | 130  | 1.7158          | 0.4062   | 0.4056    | 0.3689 |
| 1.665         | 14.0  | 140  | 1.6250          | 0.475    | 0.4543    | 0.4248 |
| 1.6115        | 15.0  | 150  | 1.5597          | 0.4875   | 0.4646    | 0.4414 |
| 1.5716        | 16.0  | 160  | 1.5112          | 0.5125   | 0.4846    | 0.4575 |
| 1.5062        | 17.0  | 170  | 1.4672          | 0.525    | 0.4932    | 0.4925 |
| 1.4655        | 18.0  | 180  | 1.4262          | 0.5312   | 0.5018    | 0.4876 |
| 1.413         | 19.0  | 190  | 1.3851          | 0.575    | 0.5253    | 0.5317 |
| 1.3758        | 20.0  | 200  | 1.3421          | 0.5625   | 0.5900    | 0.5113 |
| 1.317         | 21.0  | 210  | 1.3156          | 0.55     | 0.5835    | 0.4996 |
| 1.291         | 22.0  | 220  | 1.2712          | 0.5938   | 0.6374    | 0.5601 |
| 1.2369        | 23.0  | 230  | 1.2697          | 0.5563   | 0.5681    | 0.5250 |
| 1.2139        | 24.0  | 240  | 1.2439          | 0.5625   | 0.5733    | 0.5417 |
| 1.1766        | 25.0  | 250  | 1.2228          | 0.5938   | 0.6099    | 0.5735 |
| 1.1483        | 26.0  | 260  | 1.2464          | 0.5625   | 0.6016    | 0.5508 |
| 1.1344        | 27.0  | 270  | 1.1877          | 0.5875   | 0.6142    | 0.5718 |
| 1.0898        | 28.0  | 280  | 1.1871          | 0.6      | 0.6481    | 0.5817 |
| 1.0515        | 29.0  | 290  | 1.1553          | 0.6438   | 0.6506    | 0.6345 |
| 1.0628        | 30.0  | 300  | 1.1603          | 0.575    | 0.6209    | 0.5727 |
| 1.0257        | 31.0  | 310  | 1.1326          | 0.6125   | 0.6312    | 0.6109 |
| 1.0048        | 32.0  | 320  | 1.1450          | 0.6125   | 0.6402    | 0.6079 |
| 0.9646        | 33.0  | 330  | 1.1250          | 0.6062   | 0.6161    | 0.6004 |
| 0.9231        | 34.0  | 340  | 1.1299          | 0.6      | 0.6183    | 0.5976 |
| 0.8944        | 35.0  | 350  | 1.1312          | 0.5938   | 0.5996    | 0.5885 |
| 0.9001        | 36.0  | 360  | 1.1293          | 0.625    | 0.6358    | 0.6220 |
| 0.8587        | 37.0  | 370  | 1.1415          | 0.6062   | 0.6122    | 0.6037 |
| 0.8708        | 38.0  | 380  | 1.1171          | 0.6062   | 0.6379    | 0.5985 |
| 0.843         | 39.0  | 390  | 1.1220          | 0.625    | 0.6658    | 0.6229 |
| 0.8038        | 40.0  | 400  | 1.1144          | 0.6188   | 0.6243    | 0.6153 |
| 0.7815        | 41.0  | 410  | 1.1538          | 0.575    | 0.6042    | 0.5679 |
| 0.7289        | 42.0  | 420  | 1.1125          | 0.6062   | 0.6218    | 0.6024 |
| 0.7255        | 43.0  | 430  | 1.1401          | 0.6      | 0.6307    | 0.5947 |
| 0.7182        | 44.0  | 440  | 1.1092          | 0.6      | 0.6121    | 0.5916 |
| 0.6533        | 45.0  | 450  | 1.1219          | 0.625    | 0.6448    | 0.6268 |
| 0.6658        | 46.0  | 460  | 1.1322          | 0.6125   | 0.6272    | 0.6135 |
| 0.6293        | 47.0  | 470  | 1.1306          | 0.6      | 0.6075    | 0.5980 |
| 0.6287        | 48.0  | 480  | 1.1227          | 0.6125   | 0.6210    | 0.6099 |
| 0.622         | 49.0  | 490  | 1.1441          | 0.5938   | 0.6154    | 0.5940 |
| 0.6004        | 50.0  | 500  | 1.1119          | 0.625    | 0.6267    | 0.6206 |
| 0.606         | 51.0  | 510  | 1.1301          | 0.5938   | 0.6146    | 0.5925 |
| 0.5924        | 52.0  | 520  | 1.1552          | 0.6062   | 0.6135    | 0.6022 |
| 0.5639        | 53.0  | 530  | 1.1956          | 0.5938   | 0.6411    | 0.5945 |
| 0.5434        | 54.0  | 540  | 1.1843          | 0.5813   | 0.5925    | 0.5765 |
| 0.5479        | 55.0  | 550  | 1.1529          | 0.6125   | 0.6247    | 0.6142 |
| 0.5227        | 56.0  | 560  | 1.1730          | 0.5687   | 0.5724    | 0.5628 |
| 0.5402        | 57.0  | 570  | 1.1919          | 0.6      | 0.6075    | 0.5954 |
| 0.4971        | 58.0  | 580  | 1.1761          | 0.5938   | 0.5984    | 0.5925 |
| 0.5004        | 59.0  | 590  | 1.2305          | 0.5687   | 0.5957    | 0.5645 |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.13.3