Echo Clip

An NSFW image classification model based on the ViT architecture, pre-trained on ImageNet-21k via supervised learning and fine-tuned on 80,000 images to distinguish between normal and NSFW content.

An image classification model based on Vision Transformer architecture, pre-trained on the ImageNet-21k dataset, suitable for multi-category image classification tasks

A small-scale vision Transformer model trained using the DINOv2 method, extracting image features through self-supervised learning

Vision Transformer model pre-trained on ImageNet-21k and fine-tuned on ImageNet for image classification tasks

A Vision Transformer model pretrained on the ImageNet-21k dataset for image classification tasks.

Vision Transformer model trained using the DINOv2 method, extracting image features through self-supervised learning

An image classification model built with PyTorch and HuggingPics for recognizing gender in images

An image classification model based on Vision Transformer (ViT) architecture, specifically designed to detect whether images contain NSFW (Not Safe For Work) content.

The Hybrid Vision Transformer (ViT) model combines convolutional networks and Transformer architectures for image classification tasks, excelling on ImageNet.

This is an image classification model based on the PyTorch framework and generated using HuggingPics tools, specifically designed for gender classification tasks.