Top PyTorch Object Detection Models

Explore object detection models that use the PyTorch framework.
‍
Deploy select models (i.e. YOLOv8, CLIP) using the Roboflow Hosted API, or your own hardware using Roboflow Inference.

Object Detection

Deploy on Device with Roboflow✅

Object Detection

YOLOv10

Model Size:

Parameters:

29500000

Architecture:

YOLO

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

Object Detection

Deploy on Device with Roboflow✅

Object Detection

YOLOv8 Oriented Bounding Boxes

Model Size:

69500000.0

Parameters:

69500000

Architecture:

YOLO

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. Learn more »

Object Detection

Deploy on Device with Roboflow✅

Object Detection

YOLOv9

Model Size:

Parameters:

57300000

Architecture:

YOLO

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

Object Detection

Deploy on Device with Roboflow✅

Object Detection

DocTR

Model Size:

Parameters:

Architecture:

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

Object Detection

Deploy on Device with Roboflow✅

Object Detection

YOLO-NAS

Model Size:

67000000.0

Parameters:

Architecture:

YOLO

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

Object Detection

Deploy on Device with Roboflow✅

Object Detection

GroundingDINO

Model Size:

Parameters:

Architecture:

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

Object Detection

Deploy on Device with Roboflow✅

Object Detection

DETR

Model Size:

60000000.0

Parameters:

Architecture:

Transformers

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

Object Detection

Deploy on Device with Roboflow✅

Object Detection

YOLOv8

Model Size:

68200000.0

Parameters:

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

Deploy on Device with Roboflow✅

Object Detection

YOLOX

Model Size:

68.7

Parameters:

99.1 million parameters

Architecture:

CNN, YOLO

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

Object Detection

Deploy on Device with Roboflow✅

Object Detection

YOLOR

Model Size:

202.0

Parameters:

12,786,711 (S2D)

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

Deploy on Device with Roboflow✅

Object Detection

YOLOS

Model Size:

Parameters:

85000000

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

Deploy on Device with Roboflow✅

Object Detection

Scaled YOLOv4

Model Size:

Parameters:

Architecture:

YOLO

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

Object Detection

Deploy on Device with Roboflow✅

Object Detection

YOLOv7

Model Size:

75.6

Parameters:

Architecture:

YOLO, CNN

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

Object Detection

Deploy on Device with Roboflow✅

Object Detection

MT-YOLOv6

Model Size:

Parameters:

Architecture:

CNN, YOLO

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

Object Detection

Deploy on Device with Roboflow✅

Object Detection

YOLOv5 Oriented Bounding Boxes

Model Size:

Parameters:

7.5 million

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

Deploy on Device with Roboflow✅

Object Detection

YOLOv4 PyTorch

Model Size:

Parameters:

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

Deploy on Device with Roboflow✅

Object Detection

YOLOv3 PyTorch

Model Size:

Parameters:

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

Deploy on Device with Roboflow✅

Object Detection

EfficientDet

Model Size:

Parameters:

3.9 million

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

Deploy on Device with Roboflow✅