Models
Detectron2 vs. YOLOv4

Detectron2 vs. YOLOv4

Both Detectron2 and YOLOv4 Darknet are commonly used in computer vision projects. Below, we compare and contrast Detectron2 and YOLOv4 Darknet.

Models

icon-model

Detectron2

Detectron2 is model zoo of it's own for computer vision models written in PyTorch.
Learn more about Detectron2
icon-model

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.
Learn more about YOLOv4 Darknet
Model Type
Object Detection
--
Object Detection
--
Model Features
Item 1 Info
Item 2 Info
Architecture
--
YOLO
--
Annotation Format
Instance Segmentation
Instance Segmentation
Framework
--
PyTorch
--
Darknet
GitHub
--
View Repo
--
View Repo
GitHub Stars
28.4k+
--
--
License
Apache-2.0
--
YOLO
--
Paper
--
View Paper
--
View Paper
Training Notebook
Train on Colab
--
Train on Colab
--
Deploy Model
--
rocket icon
Deploy with Roboflow
--
rocket icon
Deploy with Roboflow
Compare Alternatives
--
Compare with...
--
Compare with...

Compare Detectron2 and YOLOv4 Darknet with Autodistill

Compare Detectron2 vs. YOLOv4

Provide your own image below to test YOLOv8 and YOLOv9 model checkpoints trained on the Microsoft COCO dataset.

COCO can detect 80 common objects, including cats, cell phones, and cars.