vit-base-nsfw-detector EU AI Act Compliance Profile

vit-base-nsfw-detector

This model is a fine-tuned version of 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

<u>New [07/30]</u>: I created a new ViT model specifically to detect NSFW/SFW images for stable diffusion usage (read the disclaimer below for the reason): AdamCodd/vit-nsfw-stable-diffusion.

Disclaimer: This model wasn't made with generative images in mind! There is no generated image in the dataset used here, and it performs significantly worse on generative images, which will require another ViT model specifically trained on generative images. Here are the model's actual scores for generative images to give you an idea:

• Loss: 0.3682 (↑ 292.95%)
• Accuracy: 0.8600 (↓ 10.91%)
• F1: 0.8654
• AUC: 0.9376 (↓ 5.75%)
• Precision: 0.8350
• Recall: 0.8980

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:

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:

from transformers import ViTImageProcessor, AutoModelForImageCla

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Tags

Library

Base Model