Models
MobileNet SSD v2 vs. Scaled-YOLOv4

MobileNet SSD v2 vs. Scaled-YOLOv4

Both MobileNet SSD v2 and Scaled YOLOv4 are commonly used in computer vision projects. Below, we compare and contrast MobileNet SSD v2 and Scaled YOLOv4.

Models

icon-model

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.
Learn more about MobileNet SSD v2
icon-model

Scaled YOLOv4

Scaled YOLOv4 is an extension of the YOLOv4 research implemented in the YOLOv5 PyTorch framework.
Learn more about Scaled YOLOv4
Model Type
Object Detection
--
Object Detection
--
Model Features
Item 1 Info
Item 2 Info
Architecture
--
YOLO
--
Frameworks
TensorFlow 1.5
--
PyTorch
--
Annotation Format
Instance Segmentation
Instance Segmentation
GitHub
--
View Repo
--
View Repo
GitHub Stars
81+
--
2k+
--
License
MIT
--
GPL-3.0
--
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 MobileNet SSD v2 and Scaled YOLOv4 with Autodistill

Compare MobileNet SSD v2 vs. Scaled-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.