Models
4M vs. YOLOR

4M vs. YOLOR

Both 4M and YOLOR are commonly used in computer vision projects. Below, we compare and contrast 4M and YOLOR.

Models

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4M

The 4M model is a versatile multimodal Transformer model developed by EPFL and Apple, capable of handling a handful of vision and language tasks.
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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.
Model Type
Multimodal Model
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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
PyTorch
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PyTorch
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Annotation Format
Instance Segmentation
Instance Segmentation
GitHub Stars
1.1k
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2k+
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License
Apache 2.0
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GPL-3.0
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Training Notebook

Compare 4M and YOLOR with Autodistill

Compare 4M vs. YOLOR

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.