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---
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
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