---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: 20E-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: 0.7178329571106095
    - name: Precision
      type: precision
      value: 0.7187025517730355
    - name: Recall
      type: recall
      value: 0.7178329571106095
    - name: F1
      type: f1
      value: 0.717743945710896
---

<!-- 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. -->

# 20E-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: 0.8245
- Accuracy: 0.7178
- Precision: 0.7187
- Recall: 0.7178
- F1: 0.7177

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.9149        | 1.0   | 194  | 1.8887          | 0.3750   | 0.3413    | 0.3750 | 0.3045 |
| 1.2903        | 2.0   | 388  | 1.2485          | 0.5792   | 0.5726    | 0.5792 | 0.5526 |
| 1.071         | 3.0   | 582  | 1.0587          | 0.6321   | 0.6258    | 0.6321 | 0.6228 |
| 1.0185        | 4.0   | 776  | 0.9817          | 0.6617   | 0.6584    | 0.6617 | 0.6553 |
| 0.894         | 5.0   | 970  | 0.9293          | 0.6869   | 0.6872    | 0.6869 | 0.6820 |
| 0.8283        | 6.0   | 1164 | 0.8881          | 0.6936   | 0.6929    | 0.6936 | 0.6905 |
| 0.8185        | 7.0   | 1358 | 0.8659          | 0.6982   | 0.7011    | 0.6982 | 0.6988 |
| 0.7499        | 8.0   | 1552 | 0.8558          | 0.7046   | 0.7050    | 0.7046 | 0.7021 |
| 0.7219        | 9.0   | 1746 | 0.8399          | 0.7124   | 0.7165    | 0.7124 | 0.7127 |
| 0.7382        | 10.0  | 1940 | 0.8300          | 0.7159   | 0.7184    | 0.7159 | 0.7145 |
| 0.6392        | 11.0  | 2134 | 0.8329          | 0.7088   | 0.7135    | 0.7088 | 0.7095 |
| 0.6549        | 12.0  | 2328 | 0.8297          | 0.7133   | 0.7135    | 0.7133 | 0.7120 |
| 0.6762        | 13.0  | 2522 | 0.8180          | 0.7156   | 0.7162    | 0.7156 | 0.7153 |
| 0.5937        | 14.0  | 2716 | 0.8271          | 0.7188   | 0.7220    | 0.7188 | 0.7190 |
| 0.569         | 15.0  | 2910 | 0.8245          | 0.7178   | 0.7175    | 0.7178 | 0.7165 |
| 0.5623        | 16.0  | 3104 | 0.8228          | 0.7165   | 0.7153    | 0.7165 | 0.7157 |
| 0.5291        | 17.0  | 3298 | 0.8238          | 0.7162   | 0.7165    | 0.7162 | 0.7156 |
| 0.5775        | 18.0  | 3492 | 0.8246          | 0.7153   | 0.7162    | 0.7153 | 0.7151 |
| 0.545         | 19.0  | 3686 | 0.8257          | 0.7178   | 0.7192    | 0.7178 | 0.7174 |
| 0.5409        | 20.0  | 3880 | 0.8245          | 0.7178   | 0.7187    | 0.7178 | 0.7177 |


### Framework versions

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