---
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
  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: 0.575
---

<!-- 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: 1.2677
- Accuracy: 0.575

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9379        | 0.97  | 13   | 1.2947          | 0.4875   |
| 0.9235        | 1.95  | 26   | 1.3397          | 0.475    |
| 0.8298        | 3.0   | 40   | 1.2971          | 0.5563   |
| 0.8883        | 3.98  | 53   | 1.3434          | 0.4875   |
| 0.8547        | 4.95  | 66   | 1.3226          | 0.475    |
| 0.8129        | 6.0   | 80   | 1.3077          | 0.5062   |
| 0.8095        | 6.97  | 93   | 1.2503          | 0.525    |
| 0.7764        | 7.95  | 106  | 1.2989          | 0.5312   |
| 0.7004        | 9.0   | 120  | 1.3383          | 0.4813   |
| 0.7013        | 9.97  | 133  | 1.3370          | 0.5125   |
| 0.6416        | 10.95 | 146  | 1.3073          | 0.5125   |
| 0.5831        | 12.0  | 160  | 1.3192          | 0.5      |
| 0.5968        | 12.97 | 173  | 1.2394          | 0.5375   |
| 0.5434        | 13.95 | 186  | 1.3389          | 0.5188   |
| 0.4605        | 15.0  | 200  | 1.2951          | 0.525    |
| 0.4674        | 15.97 | 213  | 1.2038          | 0.5687   |
| 0.3953        | 16.95 | 226  | 1.4019          | 0.5062   |
| 0.3595        | 18.0  | 240  | 1.4442          | 0.4813   |
| 0.3619        | 18.98 | 253  | 1.4213          | 0.525    |
| 0.3304        | 19.95 | 266  | 1.2937          | 0.5437   |
| 0.34          | 21.0  | 280  | 1.3024          | 0.5687   |
| 0.4215        | 21.98 | 293  | 1.4018          | 0.5375   |
| 0.3606        | 22.95 | 306  | 1.4221          | 0.5375   |
| 0.3402        | 24.0  | 320  | 1.4987          | 0.4313   |
| 0.3058        | 24.98 | 333  | 1.5120          | 0.5125   |
| 0.3047        | 25.95 | 346  | 1.5749          | 0.5      |
| 0.3616        | 27.0  | 360  | 1.4293          | 0.5188   |
| 0.3315        | 27.98 | 373  | 1.5326          | 0.5312   |
| 0.3535        | 28.95 | 386  | 1.5095          | 0.5188   |
| 0.3056        | 29.25 | 390  | 1.5366          | 0.5      |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3