Models
MobileNet V2 Classification vs. YOLOv4 Tiny

MobileNet V2 Classification vs. YOLOv4 Tiny

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

Models

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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
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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
Model Type
Classification
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Object Detection
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Model Features
Item 1 Info
Item 2 Info
Architecture
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ResNet-D, YOLO
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Frameworks
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Darknet
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
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License
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YOLO
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Training Notebook

Compare MobileNet V2 Classification and YOLOv4 Tiny with Autodistill

Models

MobileNet V2 Classification vs. YOLOv4 Tiny

.

Both

MobileNet V2 Classification

and

YOLOv4 Tiny

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

MobileNet V2 Classification

and

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

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

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

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