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 professional quantized version of the Qwen3-Omni model with 31.7B parameters. It uses advanced INT8+FP16 mixed-precision quantization technology, reducing memory usage by over 50%. It supports intelligent GPU/CPU hybrid inference, enabling large multimodal models to run efficiently on consumer-grade hardware.

MultimodalSafetensorsMultiple Languages

This is a vision Transformer model based on the DINOv3 framework, trained on the LVD-1689M dataset from the DINOv3 ViT-7B model through knowledge distillation technology. This model is specifically designed for image feature encoding and can efficiently extract image feature representations, suitable for various computer vision tasks.

Computer VisionTimm

This is a vision Transformer model based on the DINOv3 architecture, using a small configuration and trained through knowledge distillation on the LVD-1689M dataset. This model is specifically designed for efficient image feature extraction and supports various computer vision tasks such as image classification, feature map extraction, and image embedding.

Computer VisionTimm

This is a Vision Transformer image feature encoder based on the DINOv3 architecture, obtained through knowledge distillation from the 7B-parameter DINOv3 ViT model on the LVD-1689M dataset. This model is specifically designed for image feature extraction tasks and has strong visual representation capabilities.

Computer VisionTimm

MiMo-VL is a compact and powerful vision-language model that combines a native resolution ViT encoder, an MLP projector, and the MiMo-7B language model. It performs excellently in tasks such as multimodal reasoning. This model shows outstanding performance in multiple benchmark tests, has a chain-of-thought control function, and significantly improves the user experience.

MultimodalTransformers

MiMo-VL is a compact and powerful vision-language model that combines a native resolution ViT encoder, an MLP projector, and the MiMo-7B language model optimized for complex reasoning tasks. Through multi-stage pre-training and post-training, it has achieved excellent results in multiple vision-language tasks.

MultimodalTransformers

This is a ViT-L14 image encoder based on the PE-Core model by Bolya et al., which has been converted to the Birder format for image feature extraction. The model retains the original weights and architecture but removes the CLIP projection layer to output raw image embeddings. It is a general-purpose visual backbone network suitable for image classification and detection tasks.

Computer VisionPytorch

This is an ultra-lightweight Hindi speech synthesis model based on the Facebook MMS project. It adopts the VITS architecture and can convert Hindi text into high-quality, natural and fluent speech output. The model is optimized for Hindi and has efficient inference performance.

Audio ProcessingTransformersOther

A ViT image classification model using RoPE, pre-trained with CAPI and fine-tuned on ImageNet-21K, suitable for image classification and detection tasks.

Computer VisionPytorch

This is a VITS model fine-tuned for Dhivehi speech synthesis, capable of converting Dhivehi text written in Tāna script into high-quality female voice audio. It is based on Meta's MMS-TTS architecture and trained using a carefully curated synthetic Dhivehi speech dataset.

Audio ProcessingTransformersOther

ViT reg4 image encoder pretrained with Masked Image Modeling (MIM), suitable for general feature extraction or downstream vision tasks

Computer VisionPytorch

A text-to-speech synthesis model developed for the Bambara language, based on VITS architecture and Facebook MMS foundation model

Audio ProcessingTransformersOther

A Vision Transformer model fine-tuned on the beans dataset based on google/vit-base-patch16-224-in21k for image classification tasks

Computer VisionTransformers

This is a fine-tuned vision transformer model specifically designed for classifying 12 types of skin lesions. The model is based on the pre-trained ViT architecture and has been fine-tuned on approximately 70k skin lesion images. It can accurately identify various skin diseases, including melanoma and basal cell carcinoma.

Computer VisionSafetensors

This is a transformers model hosted on Hugging Face Hub, with no specific functionality explicitly stated.

Natural Language ProcessingTransformers

PHOENIX is a domain-adaptive model based on CLIP/ViT, designed to enhance patent image retrieval capabilities, particularly suitable for retrieving semantically or hierarchically related images rather than exact matches.

Computer VisionSafetensors

This is a Vision Transformer-based model for classifying facial skin types into 5 categories.

Computer VisionTransformersEnglish

A pet breed classification model fine-tuned based on Google Vision Transformer (ViT), achieving 94.45% accuracy on the Oxford-IIIT Pets dataset

Computer VisionTransformers

This paper proposes a method to reinterpret Vision Transformer (ViT) as an image segmentation model, demonstrating ViT's potential in image segmentation tasks.

Computer VisionPytorch