EfficientDet achieves the best performance in the fewest training epochs among object detection model architectures, making it a highly scalable architecture especially when operating with limited compute.
Here is an overview of the
model:
EfficientDet is a state-of-the-art object detection model for real-time object detection originally written in Tensorflow and Keras but now having implementations in PyTorch--this notebook uses the PyTorch implementation of EfficientDet. It has an EfficientNet backbone and a custom detection and classification network. Because of this backbone, EffcientDet is designed to efficiently scale from the smallest model size. The smallest EfficientDet, EfficientDet-D0 has 4 million weight parameters - it is truly tiny. EfficientDet infers in 30ms in this distribution and is considered and can be stored with only 17 megabytes of storage--making it both a small and fast model.
EfficientDet performed state-of-the-art on COCO when it was released and performs slightly better than YOLOv3.
Training EfficientDet with Custom Data: https://blog.roboflow.com/training-efficientdet-object-detection-model-with-a-custom-dataset/
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.
Roboflow offers a range of SDKs with which you can deploy your model to production.
EfficientDet
uses the
uses the
COCO JSON
annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.
You can automatically label a dataset using
EfficientDet
with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use
EfficientDet
to train a computer vision model.
Curious about how this model compares to others? Check out our model comparisons.
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