Models
YOLOR vs. EfficientNet

YOLOR vs. EfficientNet

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

Models

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YOLOR

YOLOR (You Only Learn One Representation) is an object detection model that uses both implicit and explicit knowledge to make predictions.
Learn more about YOLOR
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EfficientNet

EfficientNet is from a family of image classification models from GoogleAI that train comparatively quickly on small amounts of data, making the most of limited datasets.
Learn more about EfficientNet
Model Type
Object Detection
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Classification
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Model Features
Item 1 Info
Item 2 Info
Architecture
CNN, YOLO
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CNN
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Frameworks
PyTorch
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Keras
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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
2k+
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License
GPL-3.0
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Paper
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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
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Compare YOLOR and EfficientNet with Autodistill

Compare YOLOR vs. EfficientNet

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.