Image Classification

Classify images with two lines of code. These models are ready to go, often with pre-trained weights and exports available for mobile or server-side inference.

Looking for a dataset? Explore image classification datasets.

Deploy select models (i.e. YOLOv8, CLIP) using the Roboflow Hosted API, or your own hardware using Roboflow Inference.

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MetaCLIP

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

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YOLOv8 Classification

An image classification model built using YOLOv8.

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YOLOv5 Classification

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

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BLIP

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EfficientNet

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.

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ResNet 34

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

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ResNet-50

ResNet-50 is a popular image classification model architecture.

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AltCLIP

AltCLIP is a zero-shot image classification model.

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RemoteCLIP

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

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BioCLIP

BioCLIP is a Vision Foundation Model for the Tree of Life

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MobileCLIP

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

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SigLIP

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

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BLIPv2

BLIPv2 is a multimodal model developed by Salesforce Research.

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ALBEF

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FastViT

FastViT is a fast image classification model developed by Apple.

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ResNet 32

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

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Vision Transformer

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

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MobileNet V2 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.

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Visual Question Answering

Image Similarity

Image Captioning

Zero-shot Detection

Real-Time Vision

Image Embedding

LLMS with Vision Capabilities

Multimodal Vision

Foundation Vision

Frequently Asked Questions

What models are used for image classification?

There are a wide variety of models used for image classification. Popular choices of models for image classification tasks include YOLOv5, the Vision Transformer, and Resnet34.

What are the use cases for image classification?

Image classification is useful in any computer vision task where you need to assign content into one of a limited number of categories. Here are a few examples of real-world use cases for image classification:

Deciding whether an image contains explicit material
Classifying plant species
Identifying wildlife species
Tracking what types of vehicles enter a parking lot (i.e. cars, motorbikes)

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What is image classification?

Image classification is a computer vision task where images are assigned a label based on their contents. Only one label is assigned per image. For example, consider a dataset that classifies tree species. One photo may be given the class “birch” and another “fir”.

Where Can I Learn More About Object Detection?

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