Models
MobileNet V2 Classification vs. YOLOv4 PyTorch

MobileNet V2 Classification vs. YOLOv4 PyTorch

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

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 PyTorch

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 PyTorch.
Learn more about YOLOv4 PyTorch
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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YOLO
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Frameworks
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PyTorch
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
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4.4k+
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License
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Apache-2.0
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Training Notebook

Compare MobileNet V2 Classification and YOLOv4 PyTorch with Autodistill

Models

MobileNet V2 Classification vs. YOLOv4 PyTorch

.

Both

MobileNet V2 Classification

and

YOLOv4 PyTorch

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

MobileNet V2 Classification

and

YOLOv4 PyTorch
  MobileNet V2 Classification YOLOv4 PyTorch
Date of Release
Model Type Classification Object Detection
Architecture YOLO
GitHub Stars 4400

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 PyTorch

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 PyTorch.

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

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