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.

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

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YOLOv8

YOLOv8 is a state-of-the-art object detection and image segmentation model created by Ultralytics, the developers of YOLOv5.

Object Detection

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YOLOv9

YOLOv9 is an object detection model architecture released on February 21st, 2024.

Object Detection

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GroundingDINO

Grounding DINO is a zero-shot object detection model made by combining a Transformer-based DINO detector and grounded pre-training.

Object Detection

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YOLO-World

YOLO-World is a zero-shot object detection model.

Object Detection

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YOLOv5

A very fast and easy to use PyTorch model that achieves state of the art (or near state of the art) results.

Object Detection

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YOLO11

YOLO11 is a computer vision model that you can use for object detection, segmentation, and classification.

Object Detection

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Detectron2

Detectron2 is model zoo of it's own for computer vision models written in PyTorch.

Object Detection

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MediaPipe

Object Detection

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YOLOv8 Oriented Bounding Boxes

You can retrieve bounding boxes whose edges match an angled object by training an oriented bounding boxes object detection model, such as YOLOv8's Oriented Bounding Boxes model.

Object Detection

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LLaVA-1.5

LLaVA is an open source multimodal language model that you can use for visual question answering and has limited support for object detection.

Object Detection

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DETR

Detection Transformer (DETR) is an end-to-end object detection model implemented using the Transformer architecture.

Object Detection

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YOLOv7

YOLOv7 is a state of the art object detection model.

Object Detection

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YOLOX

YOLOX is a high-performance object detection model.

Object Detection

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YOLOv10

YOLOv10 is a real-time object detection model introduced in the paper "YOLOv10: Real-Time End-to-End Object Detection".

Object Detection

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Faster R-CNN

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.

Object Detection

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YOLOv3 PyTorch

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.

Object Detection

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YOLOv3 Keras

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.

Object Detection

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MT-YOLOv6

MT-YOLOv6 is a YOLO based model released in 2022.

Object Detection

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YOLOv4 PyTorch

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.

Object Detection

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YOLO-NAS

YOLO-NAS is an object detection model developed by Deci that achieves SOTA performances compared to YOLOv5, v7, and v8.

Object Detection

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ByteTrack

ByteTrack is a multi-object tracking computer vision algorithm.

Object Detection

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DocTR

DocTR is an Optical Character Recognition tool powered by deep learning.

Object Detection

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Scaled YOLOv4

Scaled YOLOv4 is an extension of the YOLOv4 research implemented in the YOLOv5 PyTorch framework.

Object Detection

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YOLOR

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

Object Detection

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YOLOv5 Oriented Bounding Boxes

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.

Object Detection

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YOLOS

YOLOS looks at patches of an image to to form "patch tokens", which are used in place of the traditional wordpiece tokens in NLP.

Object Detection

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4M

The 4M model is a versatile multimodal Transformer model developed by EPFL and Apple, capable of handling a handful of vision and language tasks.

Object Detection

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L2CS-Net

L2CS-Net is a gaze estimation model that enables you to calculate where someone is looking and in what direction someone is looking.

Object Detection

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MobileNet SSD v2

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

Object Detection

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CoDet

CoDet is an open vocabulary zero-shot object detection model.

Object Detection

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YOLOv4 Darknet

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.

Object Detection

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EfficientDet (D7) Tensorflow 2

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

Object Detection

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EfficientDet

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.

Object Detection

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YOLOv4 Tiny

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

Object Detection

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RTMDet

RTMDet is an efficient real-time object detector, with self-reported metrics outperforming the YOLO series. It achieves 52.8% AP on COCO with 300+ FPS on an NVIDIA 3090 GPU, making it one of the fastest and most accurate object detectors available as of writing this post.

Object Detection

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DINOv2

DINOv2 is a self-supervised method for training computer vision models developed by Meta Research and released in April 2023.

Object Detection

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Kosmos-2

Kosmos-2 is a multimodal language model capable of object detection and grounding text in images.

Object Detection

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OWLv2

OWLv2 is a transformer-based object detection model developed by Google Research. OWLv2 is the successor to OWL ViT.

Object Detection

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OWL ViT

OWL-ViT is a transformer-based object detection model developed by Google Research.

Object Detection

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GPT-4 with Vision

GPT-4 with Vision is a multimodal language model developed by OpenAI.

Object Detection

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VLPart

VLPart, developed by Meta Research, is an object detection and segmentation model that works with an open vocabulary

Object Detection

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RT-DETR

Object Detection

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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 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.

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.

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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.

Where Can I Learn More About Object Detection?

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Train YOLO-NAS - SOTA Object Detection Model - on Custom Dataset

Learn how to use YOLO-NAS in our comprehensive video guide.

How to Train YOLO-NAS on a Custom Dataset

YOLO-NAS is the latest state-of-the-art real-time object detection model. Learn how to train YOLO-NAS on your custom data.

Grounding DINO: Automated Dataset Annotation and Evaluation | SOTA Zero-Shot Object Detector

Learn how to use Grounding DINO on your own images in this YouTube guide.

Grounding DINO : SOTA Zero-Shot Object Detection

In this post, we talk about the advantages of Grounding DINO, analyze the model architecture, and provide real prompt examples

How to Train Object Detection Transformer on Custom Dataset

In this tutorial, we'll show you how to train Object Detection Transformers using DETR.

YOLOv8 Models on Universe

Explore publicly available models trained using YOLOv8.