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Models
MobileNet SSD v2 vs. YOLOv5
MobileNet SSD v2 vs. YOLOv5
Both MobileNet SSD v2 and YOLOv5 are commonly used in computer vision projects. Below, we compare and contrast MobileNet SSD v2 and YOLOv5.
MobileNet SSD v2
YOLOv5
Models
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
YOLOv5
A very fast and easy to use PyTorch model that achieves state of the art (or near state of the art) results.
Learn more about YOLOv5
Model Type
Object Detection
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Object Detection
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Model Features
Item 1 Info
Item 2 Info
Architecture
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CNN, YOLO
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Frameworks
TensorFlow 1.5
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PyTorch
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub
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View Repo
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View Repo
GitHub Stars
81+
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46k+
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License
MIT
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AGPL-3.0
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Paper
--
View Paper
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View Paper
Training Notebook
Train on Colab
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Train on Colab
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Deploy Model
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Deploy with Roboflow
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Deploy with Roboflow
Compare Alternatives
--
Compare with...
YOLOv3 Keras
YOLOv3 PyTorch
YOLOv4 PyTorch
YOLOv4 Darknet
YOLOv4 Tiny
ResNet 32
Scaled YOLOv4
YOLOR
YOLOS
YOLOX
Faster R-CNN
OpenAI CLIP
Detectron2
EfficientNet
Mask RCNN
SegFormer
MT-YOLOv6
YOLOv7
YOLOv5
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Compare with...
YOLO11
YOLO11
GPT-4o
4M
Florence 2
YOLOv9
YOLOv10
PaliGemma
YOLO-World
YOLOv8 Instance Segmentation
YOLOv8
YOLOv7 Instance Segmentation
OneFormer
MobileNet V2 Classification
MobileNet SSD v2
YOLOv3 Keras
YOLOv3 PyTorch
YOLOv4 PyTorch
YOLOv4 Darknet
YOLOv4 Tiny
ResNet 32
Scaled YOLOv4
YOLOR
YOLOS
YOLOX
Faster R-CNN
OpenAI CLIP
Detectron2
EfficientNet
Mask RCNN
SegFormer
MT-YOLOv6
YOLOv7
Compare MobileNet SSD v2 and YOLOv5 with Autodistill