Top Image Classification Models

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.

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

Classification
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YOLOv5 Classification is a version of the YOLOv5 model used in single-label and multi-label image classification. Learn more »
Classification
Deploy on Device with Roboflow✅
Classification
Classification
Classification

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CLIP (Contrastive Language-Image Pre-Training) is an impressive multimodal zero-shot image classifier that achieves impressive results in a wide range of domains with no fine-tuning. It applies the recent advancements in large-scale transformers like GPT-3 to the vision arena. Learn more »
Classification
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Classification
Classification
Classification

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CNN

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. Learn more »
Classification
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Classification
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Classification

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Architecture:

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

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Architecture:

An image classification model built using YOLOv8. Learn more »
Classification
Deploy on Device with Roboflow✅
Classification
Classification
Classification

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Architecture:

CLIP

MetaCLIP is a zero-shot classification and embedding model developed by Meta AI. Learn more »
Classification
Deploy on Device with Roboflow✅
Classification
Classification
Classification

Model Size:

MB

Parameters:

460,000

Top FPS:

Architecture:

A fast, simple convolutional neural network that gets the job done for many tasks, including classification. Learn more »
Classification
Deploy on Device with Roboflow✅
Classification
Classification
Classification

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Architecture:

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. Learn more »
Classification
Deploy on Device with Roboflow✅
Classification
Classification
Classification

Model Size:

MB

Parameters:

Top FPS:

Architecture:

A fast, simple convolutional neural network that gets the job done for many tasks, including classification. Learn more »
Classification
Deploy on Device with Roboflow✅
Classification
Classification
Classification

Model Size:

MB

Parameters:

Top FPS:

Architecture:

Classification
Deploy on Device with Roboflow✅
Classification
Classification
Classification

Model Size:

MB

Parameters:

Top FPS:

Architecture:

Classification
Deploy on Device with Roboflow✅
Classification
Classification
Classification

Model Size:

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Top FPS:

Architecture:

BLIPv2 is a multimodal model developed by Salesforce Research. Learn more »
Classification
Deploy on Device with Roboflow✅
Classification
Classification
Classification

Model Size:

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Top FPS:

Architecture:

Classification
Deploy on Device with Roboflow✅
Classification
Classification
Classification

Model Size:

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Top FPS:

Architecture:

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

Model Size:

MB

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Architecture:

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

Frequently Asked Questions

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”.

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)

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.

Where can I learn more about image classification?

See more learning resources

Image Classification Datasets and Demos

Roboflow Universe contains over 100,000 open-source models, many of which you can use for image classification tasks. Below are a few of the many models you can use.

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