transformers tensorboard 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_model
  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: 0.4125
---

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# emotion_model

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: 1.6373
- Accuracy: 0.4125

## 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: 1e-07
- train_batch_size: 10
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0746        | 1.0   | 64   | 1.6373          | 0.4125   |
| 1.0732        | 2.0   | 128  | 1.6375          | 0.4125   |
| 1.0719        | 3.0   | 192  | 1.6372          | 0.4062   |
| 1.0708        | 4.0   | 256  | 1.6372          | 0.4125   |
| 1.0698        | 5.0   | 320  | 1.6370          | 0.4062   |
| 1.0689        | 6.0   | 384  | 1.6368          | 0.4062   |
| 1.068         | 7.0   | 448  | 1.6367          | 0.4062   |
| 1.0673        | 8.0   | 512  | 1.6366          | 0.4062   |
| 1.0666        | 9.0   | 576  | 1.6366          | 0.4062   |
| 1.066         | 10.0  | 640  | 1.6366          | 0.4062   |
| 1.0656        | 11.0  | 704  | 1.6365          | 0.4062   |
| 1.0652        | 12.0  | 768  | 1.6364          | 0.4062   |
| 1.0649        | 13.0  | 832  | 1.6364          | 0.4062   |
| 1.0647        | 14.0  | 896  | 1.6364          | 0.4062   |
| 1.0646        | 15.0  | 960  | 1.6364          | 0.4062   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
