Models
YOLO11 vs. YOLOv4 PyTorch

YOLO11 vs. YOLOv4 PyTorch

Both YOLO11 and YOLOv4 PyTorch are commonly used in computer vision projects. Below, we compare and contrast YOLO11 and YOLOv4 PyTorch.

Models

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YOLO11

YOLO11 is a computer vision model that you can use for object detection, segmentation, and classification.
Learn more about YOLO11
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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
Object Detection
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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
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View Repo
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View Repo
GitHub Stars
29000
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4.4k+
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License
AGPL-3.0
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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 YOLO11 and YOLOv4 PyTorch with Autodistill

Compare YOLO11 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.