Models
YOLOv4 Tiny vs. MobileNet V2 Classification

YOLOv4 Tiny vs. MobileNet V2 Classification

Both YOLOv4 Tiny and MobileNet V2 Classification are commonly used in computer vision projects. Below, we compare and contrast YOLOv4 Tiny and MobileNet V2 Classification.

Models

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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
Learn more about YOLOv4 Tiny
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MobileNet V2 Classification

MobileNet is a GoogleAI model well-suited for on-device, real-time classification (distinct from MobileNetSSD, Single Shot Detector). This implementation leverages transfer learning from ImageNet to your dataset.
Learn more about MobileNet V2 Classification
Model Type
Object Detection
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Classification
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Model Features
Item 1 Info
Item 2 Info
Architecture
ResNet-D, YOLO
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Frameworks
Darknet
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
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License
YOLO
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Training Notebook

Compare YOLOv4 Tiny and MobileNet V2 Classification with Autodistill

Models

YOLOv4 Tiny vs. MobileNet V2 Classification

.

Both

YOLOv4 Tiny

and

MobileNet V2 Classification

are commonly used in computer vision projects. Below, we compare and contrast

YOLOv4 Tiny

and

MobileNet V2 Classification
  YOLOv4 Tiny MobileNet V2 Classification
Date of Release Nov 09, 2020
Model Type Object Detection Classification
Architecture ResNet-D, YOLO
GitHub Stars

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

How to AugmentHow to LabelHow to Plot PredictionsHow to Filter PredictionsHow to Create a Confusion Matrix

MobileNet V2 Classification

MobileNet is a GoogleAI model well-suited for on-device, real-time classification (distinct from MobileNetSSD, Single Shot Detector). This implementation leverages transfer learning from ImageNet to your dataset.

How to AugmentHow to LabelHow to Plot PredictionsHow to Filter PredictionsHow to Create a Confusion Matrix

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