---
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
  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: 0.475
---

<!-- 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: 1.5599
- Accuracy: 0.475

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 5    | 2.0884          | 0.1125   |
| 2.08          | 2.0   | 10   | 2.0750          | 0.1437   |
| 2.08          | 3.0   | 15   | 2.0519          | 0.2125   |
| 2.0091        | 4.0   | 20   | 2.0177          | 0.225    |
| 2.0091        | 5.0   | 25   | 1.9777          | 0.2625   |
| 1.8779        | 6.0   | 30   | 1.9381          | 0.3125   |
| 1.8779        | 7.0   | 35   | 1.8990          | 0.3438   |
| 1.7355        | 8.0   | 40   | 1.8592          | 0.3688   |
| 1.7355        | 9.0   | 45   | 1.8217          | 0.3812   |
| 1.598         | 10.0  | 50   | 1.7844          | 0.4      |
| 1.598         | 11.0  | 55   | 1.7536          | 0.4062   |
| 1.4689        | 12.0  | 60   | 1.7217          | 0.4188   |
| 1.4689        | 13.0  | 65   | 1.7019          | 0.4188   |
| 1.3534        | 14.0  | 70   | 1.6773          | 0.4188   |
| 1.3534        | 15.0  | 75   | 1.6614          | 0.425    |
| 1.2526        | 16.0  | 80   | 1.6448          | 0.4562   |
| 1.2526        | 17.0  | 85   | 1.6306          | 0.45     |
| 1.1657        | 18.0  | 90   | 1.6201          | 0.4562   |
| 1.1657        | 19.0  | 95   | 1.6067          | 0.4562   |
| 1.0918        | 20.0  | 100  | 1.5992          | 0.45     |
| 1.0918        | 21.0  | 105  | 1.5889          | 0.4562   |
| 1.0311        | 22.0  | 110  | 1.5852          | 0.4562   |
| 1.0311        | 23.0  | 115  | 1.5767          | 0.4625   |
| 0.9814        | 24.0  | 120  | 1.5733          | 0.45     |
| 0.9814        | 25.0  | 125  | 1.5688          | 0.4625   |
| 0.9439        | 26.0  | 130  | 1.5643          | 0.4562   |
| 0.9439        | 27.0  | 135  | 1.5620          | 0.4625   |
| 0.918         | 28.0  | 140  | 1.5599          | 0.475    |
| 0.918         | 29.0  | 145  | 1.5586          | 0.4625   |
| 0.9044        | 30.0  | 150  | 1.5582          | 0.4562   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3