Top Object Detection Models

State of the art object detection models to localize subjects in images. From YOLOv5 to MobileNet, we have the most popular models in easy to use formats.

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Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

Top FPS:

Architecture:

YOLO, CNN

YOLOv8 is a state-of-the-art object detection and image segmentation model created by Ultralytics, the developers of YOLOv5. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

14.1

MB

Parameters:

7.2 million

Top FPS:

140

Architecture:

CNN, YOLO

A very fast and easy to use PyTorch model that achieves state of the art (or near state of the art) results. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

7.5 million

Top FPS:

Architecture:

CNN, YOLO

YOLOv5-OBB is a variant of YOLOv5 that supports oriented bounding boxes. This model is designed to yield predictions that better fit objects that are positioned at an angle. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

Top FPS:

Architecture:

Detectron2 is model zoo of it's own for computer vision models written in PyTorch. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

Top FPS:

Architecture:

YOLOv4 has emerged as the best real time object detection model. YOLOv4 carries forward many of the research contributions of the YOLO family of models along with new modeling and data augmentation techniques. This implementation is in Darknet. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

Top FPS:

Architecture:

One of the most accurate object detection algorithms but requires a lot of power at inference time. A good choice if you can do processing asynchronously on a server. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

68.7

MB

Parameters:

9 million

Top FPS:

Architecture:

CNN, YOLO

YOLOX is a high-performance object detection model. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

75.6

MB

Parameters:

Top FPS:

161

Architecture:

YOLO, CNN

YOLOv7 is a state of the art object detection model. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

Top FPS:

Architecture:

Though it is no longer the most accurate object detection algorithm, YOLO v3 is still a very good choice when you need real-time detection while maintaining excellent accuracy. Keras implementation. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

Top FPS:

Architecture:

YOLO

Though it is no longer the most accurate object detection algorithm, YOLO v3 is still a very good choice when you need real-time detection while maintaining excellent accuracy. PyTorch version. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

Top FPS:

520

Architecture:

CNN, YOLO

MT-YOLOv6 is a YOLO based model released in 2022. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

Top FPS:

Architecture:

YOLO

YOLOv4 has emerged as the best real time object detection model. YOLOv4 carries forward many of the research contributions of the YOLO family of models along with new modeling and data augmentation techniques. This implementation is in PyTorch. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

Top FPS:

Architecture:

Scaled YOLOv4 is an extension of the YOLOv4 research implemented in the YOLOv5 PyTorch framework. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

Top FPS:

Architecture:

This architecture provides good realtime results on limited compute. It's designed to run in realtime (30 frames per second) even on mobile devices. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

202.0

MB

Parameters:

12,786,711 (S2D)

Top FPS:

106

Architecture:

CNN, YOLO

YOLOR (You Only Learn One Representation) is an object detection model that uses both implicit and explicit knowledge to make predictions. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

77 million

Top FPS:

8

Architecture:

A scalable, state of the art object detection model, implemented here within the TensorFlow 2 Object Detection API. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

3.9 million

Top FPS:

97

Architecture:

EfficientDet achieves the best performance in the fewest training epochs among object detection model architectures, making it a highly scalable architecture especially when operating with limited compute. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

Top FPS:

Architecture:

Transformer, YOLO

YOLOS looks at patches of an image to to form "patch tokens", which are used in place of the traditional wordpiece tokens in NLP. Learn more »
Object Detection
Object Detection
fEATURED
Object Detection
Object Detection

Model Size:

MB

Parameters:

Top FPS:

34

Architecture:

ResNet-D, YOLO

The tiny and fast version of YOLOv4 - good for training and deployment on limited compute resources, and getting a feel for your dataset Learn more »

Frequently Asked Questions

What is object detection?

Object detection is a computer vision solution that focuses on identifying the location of objects in an image or video. Each identified object is assigned a label that represents its contents. Using object detection, you can also count the number of times different objects appear in an image.

What are the use cases for object detection?

Object detection has many use cases, including:

  • Identifying whether someone is wearing the requisite safety equipment in a controlled environment (i.e. a construction site or a medical area).
  • Finding objects on a road to help guide an autonomous car.
  • Identifying animals in a wildlife reserve.
  • Counting pills to be dispensed into bottles for pharmacies.

What models are used for object detection?

The YOLO family of models (i.e. YOLOv7, YOLOv7) are commonly used in object detection use cases. YOLO has been developed and refined over a years-long period and is still in active development. The latest model, YOLOv7, achieves state-of-the-art performance on object detection in the MS COCO dataset. Other models like Detectron2 and EfficientDet are also used in object detection.

Where can I learn more about object detection?

See more learning resources

Datasets and Demos for Object Detection

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

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