This is a housing condition classifier fine-tuned based on the Google ViT-base model, which can classify housing images into four categories: good, unknown, dilapidated, and medium. The model was trained on a dataset of 935 housing images, and the accuracy on the validation set reached 81.2%.

Computer VisionTransformersEnglish

This is a version of the Google Gemma 3N E4B IT model with FP8 dynamic quantization. By quantizing the weights and activation values to the FP8 data type, the inference efficiency is significantly improved while maintaining the performance of the original model. It supports multimodal inputs (text, images, audio, videos) and text outputs.

MultimodalSafetensorsMultiple Languages

This is a binary classification image model based on the google/siglip2-base-patch16-224 architecture, specifically designed to detect fractures in skeletal X-ray images. This model has important application value in medical diagnosis, clinical triage, and radiology assistance systems.

Computer VisionTransformersEnglish

WikiArt-Style is a visual model based on the SiglipForImageClassification architecture, specifically designed for art image classification. This model is fine-tuned from google/siglip2-base-patch16-224 and can accurately classify art images into 137 different painting style categories.

Computer VisionTransformersMultiple Languages

Gemma 3 is a series of lightweight open models launched by Google, supporting multimodal input (text and images), with a 128K large context window, suitable for various tasks such as question answering and summarization.

MultimodalTransformersEnglish

This model is a facial expression classifier fine-tuned on the FER2013 dataset based on Google's ViT base model, capable of classifying images into four facial expressions.

Computer VisionSafetensors

An image classification model based on Google Vision Transformer (ViT) architecture for detecting criminal activities in surveillance camera images, with approximately 83% accuracy.

Computer VisionSafetensors

An image classification model based on Google's Vision Transformer (ViT) architecture, specifically designed to identify five hairstyle types (curly, dreadlocks, twists, straight, wavy) from facial images with 93% accuracy.

Computer VisionSafetensors

A multimodal table detection model fine-tuned from google/paligemma-3b-mix-448, specialized in identifying table regions in images

MultimodalTransformers

An image classification model fine-tuned based on Google Vision Transformer (ViT) architecture, used to distinguish AI-generated images from real images

Computer VisionTransformers

An image classification model fine-tuned on the cats_vs_dogs dataset using Google's ViT model, designed to distinguish between images of cats and dogs.

Computer VisionTransformers

This is a human action recognition model based on the Google Vision Transformer (ViT) architecture, fine-tuned on the Human_Action_Recognition dataset. The model can classify human action images into 15 categories, including daily actions such as making a phone call, clapping, riding a bike, and dancing.

Computer VisionTransformersEnglish

A fine-tuned model based on Google's Vision Transformer (ViT) architecture, specifically designed for distinguishing between blurry and clear images

Computer VisionTransformers

This model uses Google's ViT-base architecture to predict the presence of beards in facial images, achieving 100% accuracy on the test set.

Computer VisionTransformers

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on a blurry image dataset, designed to distinguish between blurry and clear images.

Computer VisionTransformers

This model is a medical image classification model specifically fine-tuned on the breast invasive ductal carcinoma dataset based on the Google Vision Transformer (ViT) architecture. The model achieved an accuracy of 87.31% on the evaluation set and is specifically used for the intelligent recognition and analysis of breast cancer pathological images.

Computer VisionTransformers

This is a Riffusion model fine-tuned on the google/MusicCaps dataset, capable of generating music or music-related images based on text prompts.

MultimodalTensorboardEnglish