nsfw_image_detection and vit-base-patch16-224 are both image-classification models. See each entry for specifics.
Vision Transformer (ViT) fine-tuned for binary NSFW vs. safe image classification. Provides a single classifier for flagging potentially unsafe image content without category-level labeling. Built on ViT-base architecture and fine-tuned on a curated dataset of safe and unsafe images.
Google's ViT-Base (Vision Transformer base model) with 16×16 pixel patch size trained at 224px resolution on ImageNet-21k and fine-tuned on ImageNet-1k. The paper introducing ViTs demonstrated that pure transformer architectures without convolutional inductive bias can match CNNs on image classification when trained on sufficient data. Widely used as a starting backbone for image classification fine-tuning.
Pick based on your compute budget and specific task requirements.