---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_classification_adjusted
  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: 0.8875
---

<!-- 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_adjusted

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: 0.8104
- Accuracy: 0.8875

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 60
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.0787        | 1.0   | 20   | 0.1625   | 2.0753          |
| 2.073         | 2.0   | 40   | 0.1187   | 2.0737          |
| 2.0599        | 3.0   | 60   | 0.1938   | 2.0585          |
| 2.0363        | 4.0   | 80   | 0.1938   | 2.0368          |
| 2.0051        | 5.0   | 100  | 0.2625   | 1.9921          |
| 1.9348        | 6.0   | 120  | 0.3375   | 1.9185          |
| 1.8466        | 7.0   | 140  | 0.375    | 1.8056          |
| 1.755         | 8.0   | 160  | 0.4313   | 1.7292          |
| 1.676         | 9.0   | 180  | 0.45     | 1.6674          |
| 1.6244        | 10.0  | 200  | 0.475    | 1.6237          |
| 1.5661        | 11.0  | 220  | 0.5062   | 1.5973          |
| 1.5252        | 12.0  | 240  | 0.5      | 1.5262          |
| 1.4729        | 13.0  | 260  | 0.55     | 1.5050          |
| 1.4203        | 14.0  | 280  | 0.55     | 1.4784          |
| 1.364         | 15.0  | 300  | 0.525    | 1.5131          |
| 1.3262        | 16.0  | 320  | 0.5125   | 1.4776          |
| 1.3102        | 17.0  | 340  | 0.5563   | 1.4200          |
| 1.2595        | 18.0  | 360  | 0.5563   | 1.4329          |
| 1.2188        | 19.0  | 380  | 0.5375   | 1.4213          |
| 1.1991        | 20.0  | 400  | 0.525    | 1.4077          |
| 1.1526        | 21.0  | 420  | 0.6062   | 1.3625          |
| 1.1225        | 22.0  | 440  | 0.5437   | 1.3745          |
| 1.1283        | 23.0  | 460  | 0.5375   | 1.3677          |
| 1.0856        | 24.0  | 480  | 0.5625   | 1.3283          |
| 1.0559        | 25.0  | 500  | 0.5687   | 1.3440          |
| 1.0102        | 26.0  | 520  | 0.5437   | 1.3357          |
| 0.9915        | 27.0  | 540  | 0.5813   | 1.3377          |
| 0.9807        | 28.0  | 560  | 0.55     | 1.3824          |
| 0.9382        | 29.0  | 580  | 0.4938   | 1.4468          |
| 0.9857        | 30.0  | 600  | 0.8125   | 0.9923          |
| 0.9956        | 31.0  | 620  | 0.7625   | 1.0361          |
| 0.9875        | 32.0  | 640  | 0.775    | 1.0310          |
| 0.9582        | 33.0  | 660  | 0.7625   | 1.0572          |
| 0.9649        | 34.0  | 680  | 0.8063   | 0.9725          |
| 0.9099        | 35.0  | 700  | 0.7562   | 1.0355          |
| 0.9339        | 36.0  | 720  | 0.7937   | 1.0129          |
| 0.9045        | 37.0  | 740  | 0.7562   | 1.0315          |
| 0.8903        | 38.0  | 760  | 0.8187   | 0.9923          |
| 0.8799        | 39.0  | 780  | 0.7625   | 1.0386          |
| 0.8664        | 40.0  | 800  | 0.7438   | 1.0626          |
| 0.8351        | 41.0  | 820  | 0.7688   | 0.9885          |
| 0.8514        | 42.0  | 840  | 0.7875   | 0.9975          |
| 0.857         | 43.0  | 860  | 0.75     | 1.0169          |
| 0.8331        | 44.0  | 880  | 0.7937   | 0.9763          |
| 0.8093        | 45.0  | 900  | 0.7937   | 0.9645          |
| 0.8303        | 46.0  | 920  | 0.8      | 0.9880          |
| 0.8077        | 47.0  | 940  | 0.8063   | 1.0094          |
| 0.8082        | 48.0  | 960  | 0.7937   | 0.9757          |
| 0.8088        | 49.0  | 980  | 0.7438   | 1.0451          |
| 0.7985        | 50.0  | 1000 | 0.7875   | 0.9850          |
| 0.8013        | 51.0  | 1020 | 0.7688   | 1.0362          |
| 0.7882        | 52.0  | 1040 | 0.775    | 1.0007          |
| 0.8051        | 53.0  | 1060 | 0.7438   | 1.0314          |
| 0.812         | 54.0  | 1080 | 0.8      | 0.9782          |
| 0.7895        | 55.0  | 1100 | 0.725    | 1.0396          |
| 0.8012        | 56.0  | 1120 | 0.7688   | 0.9894          |
| 0.7973        | 57.0  | 1140 | 0.7875   | 0.9981          |
| 0.7946        | 58.0  | 1160 | 0.8063   | 0.9754          |
| 0.8437        | 59.0  | 1180 | 0.85     | 0.8544          |
| 0.8489        | 60.0  | 1200 | 0.7991   | 0.9062          |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0