---
library_name: transformers
license: apache-2.0
base_model: Falconsai/nsfw_image_detection
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-videogames
  results: []
---

<!-- 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. -->

# vit-videogames

This model is a fine-tuned version of [Falconsai/nsfw_image_detection](https://huggingface.co/Falconsai/nsfw_image_detection) on the Bingsu/Gameplay_Images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0083
- Accuracy: 0.998

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0185        | 0.5   | 500  | 0.0242          | 0.995    |
| 0.0082        | 1.0   | 1000 | 0.0191          | 0.995    |
| 0.0072        | 1.5   | 1500 | 0.0212          | 0.9945   |
| 0.0041        | 2.0   | 2000 | 0.0143          | 0.997    |
| 0.0055        | 2.5   | 2500 | 0.0154          | 0.9965   |
| 0.004         | 3.0   | 3000 | 0.0128          | 0.9975   |
| 0.0016        | 3.5   | 3500 | 0.0109          | 0.9975   |
| 0.0014        | 4.0   | 4000 | 0.0089          | 0.998    |
| 0.0021        | 4.5   | 4500 | 0.0084          | 0.998    |
| 0.0005        | 5.0   | 5000 | 0.0083          | 0.998    |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0