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---
metrics:
- accuracy
pipeline_tag: image-classification
base_model: vit-base-patch16-384
model-index:
- name: vit-base-nsfw-detector
results:
- task:
type: image-classification
name: Image Classification
metrics:
- type: accuracy
value: 0.9654
name: Accuracy
- type: AUC
value: 0.9948
- type: loss
value: 0.0937
name: Loss
---
# 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 2000 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:
[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). |