Vision Transformer Alternatives

YOLOv5 Classification is a version of the YOLOv5 model used in single-label and multi-label image classification.

An image classification model built using YOLOv8.

The Vision Transformer leverages powerful natural language processing embeddings (BERT) and applies them to images.

EfficientNet is from a family of image classification models from GoogleAI that train comparatively quickly on small amounts of data, making the most of limited datasets.

SigLIP is an image embedding model defined in the "Sigmoid Loss for Language Image Pre-Training" paper.

MetaCLIP is a zero-shot classification and embedding model developed by Meta AI.

A fast, simple convolutional neural network that gets the job done for many tasks, including classification.

MobileNet is a GoogleAI model well-suited for on-device, real-time classification (distinct from MobileNetSSD, Single Shot Detector). This implementation leverages transfer learning from ImageNet to your dataset.

A fast, simple convolutional neural network that gets the job done for many tasks, including classification.

FastViT is a fast image classification model developed by Apple.

BLIPv2 is a multimodal model developed by Salesforce Research.

MobileCLIP is an image embedding model developed by Apple and introduced in the "MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training" paper

BioCLIP is a Vision Foundation Model for the Tree of Life

RemoteCLIP is a zero-shot classification model for remote sensing.

AltCLIP is a zero-shot image classification model.