Models
4M vs. Faster R-CNN

4M vs. Faster R-CNN

Both 4M and Faster R-CNN are commonly used in computer vision projects. Below, we compare and contrast 4M and Faster R-CNN.

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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Faster R-CNN

One of the most accurate object detection algorithms but requires a lot of power at inference time. A good choice if you can do processing asynchronously on a server.
Learn more about Faster R-CNN
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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Frameworks
PyTorch
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TensorFlow 1.5
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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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7.5k+
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License
Apache 2.0
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MIT
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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 Faster R-CNN with Autodistill

Compare 4M vs. Faster R-CNN

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.