Models
4M vs. YOLOv4 PyTorch

4M vs. YOLOv4 PyTorch

Both 4M and YOLOv4 PyTorch are commonly used in computer vision projects. Below, we compare and contrast 4M and YOLOv4 PyTorch.

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.
Learn more about 4M
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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
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
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View Repo
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View Repo
GitHub Stars
1.1k
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4.4k+
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License
Apache 2.0
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Apache-2.0
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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 4M and YOLOv4 PyTorch with Autodistill

Compare 4M vs. YOLOv4 PyTorch

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.