---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: finetuned-affecthq
  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: 0.7179302910528207
    - name: Precision
      type: precision
      value: 0.7173911115103917
    - name: Recall
      type: recall
      value: 0.7179302910528207
    - name: F1
      type: f1
      value: 0.7166821507529032
---

<!-- 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. -->

# finetuned-affecthq

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: 0.8116
- Accuracy: 0.7179
- Precision: 0.7174
- Recall: 0.7179
- F1: 0.7167

## 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-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 17
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.5413        | 1.0   | 174  | 1.4810          | 0.4898   | 0.4867    | 0.4898 | 0.4409 |
| 1.0367        | 2.0   | 348  | 1.0571          | 0.6155   | 0.6172    | 0.6155 | 0.6041 |
| 0.9534        | 3.0   | 522  | 0.9673          | 0.6475   | 0.6476    | 0.6475 | 0.6375 |
| 0.8532        | 4.0   | 696  | 0.9056          | 0.6748   | 0.6710    | 0.6748 | 0.6704 |
| 0.8211        | 5.0   | 870  | 0.8707          | 0.6903   | 0.6912    | 0.6903 | 0.6836 |
| 0.7797        | 6.0   | 1044 | 0.8472          | 0.7050   | 0.7050    | 0.7050 | 0.7019 |
| 0.7816        | 7.0   | 1218 | 0.8298          | 0.7111   | 0.7099    | 0.7111 | 0.7096 |
| 0.7135        | 8.0   | 1392 | 0.8186          | 0.7111   | 0.7116    | 0.7111 | 0.7105 |
| 0.6697        | 9.0   | 1566 | 0.8143          | 0.7140   | 0.7124    | 0.7140 | 0.7126 |
| 0.6765        | 10.0  | 1740 | 0.8116          | 0.7179   | 0.7174    | 0.7179 | 0.7167 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2