
# vit-base-nsfw-detector

This model is a fine-tuned version of [vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on around 25_000 images (drawings, photos...).
It achieves the following results on the evaluation set:
- Loss: 0.0937
- Accuracy: 0.9654


## Model description

The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.

## Intended uses & limitations

There are two classes: SFW and NSFW. The model has been trained to be restrictive and therefore classify "sexy" images as NSFW. That is, if the image shows cleavage or too much skin, it will be classified as NSFW. This is normal.

Usage for a local image:
```python
from transformers import pipeline
from PIL import Image

img = Image.open("<path_to_image_file>")
predict = pipeline("image-classification", model="AdamCodd/vit-base-nsfw-detector")
predict(img)
```

Usage for a distant image:
```python
from transformers import ViTImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = ViTImageProcessor.from_pretrained('AdamCodd/vit-base-nsfw-detector')
model = AutoModelForImageClassification.from_pretrained('AdamCodd/vit-base-nsfw-detector')
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits

predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
# Predicted class: sfw
```

The model has been trained on a variety of images (realistic, 3D, drawings), yet it is not perfect and some images may be wrongly classified as NSFW when they are not.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 1

### Training results

- Validation Loss: 0.0937
- Accuracy: 0.9654, 
- AUC: 0.9948

[Confusion matrix](https://huggingface.co/AdamCodd/vit-base-nsfw-detector/resolve/main/confusion_matrix.png) (eval):

[1076   37]

[  60 1627]

### Framework versions

- Transformers 4.36.2
- Evaluate 0.4.1

If you want to support me, you can [here](https://ko-fi.com/adamcodd).
{"metrics": ["accuracy"], "pipeline_tag": "image-classification", "base_model": "google/vit-base-patch16-384"}
transformers onnx safetensors vit image-classification base_model:google/vit-base-patch16-384 model-index autotrain_compatible endpoints_compatible has_space region:us