Models
4M vs. YOLOv10

4M vs. YOLOv10

Both 4M and YOLOv10 are commonly used in computer vision projects. Below, we compare and contrast 4M and YOLOv10.

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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YOLOv10

YOLOv10 is a real-time object detection model introduced in the paper "YOLOv10: Real-Time End-to-End Object Detection".
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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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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7800
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License
Apache 2.0
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GNU Affero General Public
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Training Notebook

Compare 4M and YOLOv10 with Autodistill

Compare 4M vs. YOLOv10

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.