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Image feature extraction and ImageNet category prediction using ResNet50-v1d network, provided by GluonCV
Overview
This model provides intermediate image feature extraction functionality for image classification. It can also provide top-5 category predictions out of 1000 classes on ImageNet.
This network is one of the best models that are both highly efficient and accurate. As a result, it also provides high-quality features for various tasks.
Highlights
- This model can extract high-quality image features efficiently.
- This model can predict top-5 predictions on ImageNet.
- The state-of-the-art performance with accuracy of 79.15 vs. 75.3 in the original paper (https://arxiv.org/abs/1512.03385)
Details
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Deployed on AWS
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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.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Initial release
Additional details
Inputs
- Summary
Supported content types are image/jpeg, image/png and image/bmp.
AWS APIs can be used to invoke the model after endpoint creation, e.g., using aws-cli:
aws sagemaker-runtime invoke-endpoint --endpoint-name your_endpoint_name --body fileb://img.jpg --accept image/jpeg --custom-attributes '{"feature": "flat"}' feat.out
This command will extract 2048-dim feature prior to fully-connected layer.
Supported layers: ('conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3', 'layer4', 'avgpool', 'flat', 'fc'), if you don't speficy custom-attributes, this model will return top-5 predictions.
- Input MIME type
- image/jpeg, image/png, image/bmp
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
Model supported is available from GluonCV. Search for questions and open new issues to ask questions.
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