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This is a Object Detection Answering model from TensorFlow Hub
Overview
This is an object detection model from TensorFlow Hub . It takes an image as input and returns bounding boxes for the objects in the image. The model is pre-trained on COCO 2017 which comprises images with multiple objects and the task is to identify the objects and their positions in the image. A list of the objects that the model can identify is given at the end of the page. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.
Highlights
- This is an Object Detection model from TensorFlow Hub: https://tfhub.dev/tensorflow/efficientdet/d1/1
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g4dn.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g4dn.xlarge instance type, real-time mode | $0.00 |
ml.p2.xlarge Inference (Batch) Recommended | Model inference on the ml.p2.xlarge instance type, batch mode | $0.00 |
ml.m5.large Inference (Real-Time) | Model inference on the ml.m5.large instance type, real-time mode | $0.00 |
ml.m5.xlarge Inference (Real-Time) | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.00 |
ml.c5.xlarge Inference (Real-Time) | Model inference on the ml.c5.xlarge instance type, real-time mode | $0.00 |
ml.c5.2xlarge Inference (Real-Time) | Model inference on the ml.c5.2xlarge instance type, real-time mode | $0.00 |
ml.p2.xlarge Inference (Real-Time) | Model inference on the ml.p2.xlarge instance type, real-time mode | $0.00 |
ml.p3.2xlarge Inference (Real-Time) | Model inference on the ml.p3.2xlarge instance type, real-time mode | $0.00 |
ml.m5.large Inference (Batch) | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $0.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
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Version release notes
This GPU version supports model run on GPU instance types
Additional details
Inputs
- Summary
The input is an image.
- Input MIME type
- application/x-image
https://jumpstart-cache-prod-us-west-2.s3-us-west-2.amazonaws.com/tensorflow-metadata/assets/Naxos_Taverna.jpg
https://jumpstart-cache-prod-us-west-2.s3-us-west-2.amazonaws.com/tensorflow-metadata/assets/Naxos_Taverna.jpg
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Customer reviews
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1 external reviews
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Ziggy M.
Delivers Speed and Accuracy
Reviewed on Mar 05, 2024
Review provided by G2
What do you like best about the product?
EfficientFast is the best thing about the EfficientDet D1 SSD because of the superb balance that it gets to strike between speed and accuracy. It gives me the power to quickly analyze photo and detect objects with excellent results without harming the quality of the delivered reporting. I can't put the significance of this more. This should be my essential requirement at work since I need to make a real-time analysis of images data.
What do you dislike about the product?
ThEfficientDet D1 SSD is not up to par as the general object detection tasks. And it can conducts a less accurate detention when the detected objects are very small or complex. This might pose a limitation if the project is specified and aspects related to it are very important.
What problems is the product solving and how is that benefiting you?
SSD EfficientDet D1 has accelerated my working pace and given me an ability to extract any important information from image-based data at very short order. Its speed and accuracy has by bus help me save some time and resources that will play an important role in my work.