transformers safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224-in21k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_image_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: 0.5875
---

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# emotion_image_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: 1.3343
- Accuracy: 0.5875

## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 160
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.87  | 4    | 2.0221          | 0.1      |
| No log        | 1.96  | 9    | 1.6982          | 0.25     |
| No log        | 2.83  | 13   | 1.7868          | 0.225    |
| No log        | 3.91  | 18   | 1.6731          | 0.2625   |
| No log        | 5.0   | 23   | 1.6196          | 0.175    |
| No log        | 5.87  | 27   | 1.5399          | 0.3      |
| No log        | 6.96  | 32   | 1.5348          | 0.375    |
| No log        | 7.83  | 36   | 1.6157          | 0.3125   |
| No log        | 8.91  | 41   | 1.4275          | 0.45     |
| No log        | 10.0  | 46   | 1.3832          | 0.425    |
| No log        | 10.87 | 50   | 1.4440          | 0.425    |
| No log        | 11.96 | 55   | 1.5841          | 0.4375   |
| No log        | 12.83 | 59   | 1.4398          | 0.4625   |
| No log        | 13.91 | 64   | 1.4413          | 0.475    |
| No log        | 15.0  | 69   | 1.3143          | 0.5375   |
| No log        | 15.87 | 73   | 1.3667          | 0.5625   |
| No log        | 16.96 | 78   | 1.4028          | 0.5      |
| No log        | 17.83 | 82   | 1.4485          | 0.5375   |
| No log        | 18.91 | 87   | 1.9334          | 0.3875   |
| No log        | 20.0  | 92   | 1.4611          | 0.55     |
| No log        | 20.87 | 96   | 1.3279          | 0.5875   |
| No log        | 21.96 | 101  | 1.6526          | 0.45     |
| No log        | 22.83 | 105  | 1.4921          | 0.4875   |
| No log        | 23.91 | 110  | 1.3962          | 0.5875   |
| No log        | 25.0  | 115  | 1.7038          | 0.4375   |
| No log        | 25.87 | 119  | 1.5210          | 0.55     |
| No log        | 26.09 | 120  | 1.5141          | 0.5125   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
