Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
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Image Recognition with PyTorch Resnet
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Image Recognition and Classification with PyTorch Vision Resnet | Rearc
Product Overview
This product is an Image Recognition and Classification model from PyTorch Hub. It takes an image as input and classifies the image into one of the multiple labels. How was the model trained? The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The ImageNet project is a large visual database designed for use in visual object recognition software research. Do you want to create an Image Classification Web or Mobile app? Look no further and subscribe to this model to get started! Please don't hesitate in reaching out to us by clicking: "Contact Seller" above. We are excited to get to know your use-cases and help you out by providing training data, model customizations, and more!
Key Data
Version
By
Categories
Type
Model Package
Highlights
Deep Residual Learning for Image Recognition from PyTorch Hub: https://pytorch.org/hub/pytorch_vision_resnet/
Download the ImageNet Labels here: https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt
Interested in Training Datasets? Take a look at our 400+ available data products here: https://aws.amazon.com/marketplace/seller-profile?id=a8a86da2-b2d1-4fae-992d-03494e90590b
Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us
Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$0.00/hr
running on ml.m4.xlarge
Model Batch Transform$0.00/hr
running on ml.m4.xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.24/host/hr
running on ml.m4.xlarge
SageMaker Batch Transform$0.24/host/hr
running on ml.m4.xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
---|---|---|
ml.m4.4xlarge | $0.00 | |
ml.m5.4xlarge | $0.00 | |
ml.m4.16xlarge | $0.00 | |
ml.m5.2xlarge | $0.00 | |
ml.p3.16xlarge | $0.00 | |
ml.m4.2xlarge | $0.00 | |
ml.c5.2xlarge | $0.00 | |
ml.p3.2xlarge | $0.00 | |
ml.c4.2xlarge | $0.00 | |
ml.m4.10xlarge | $0.00 | |
ml.c4.xlarge | $0.00 | |
ml.m5.24xlarge | $0.00 | |
ml.c5.xlarge | $0.00 | |
ml.p2.xlarge | $0.00 | |
ml.m5.12xlarge | $0.00 | |
ml.p2.16xlarge | $0.00 | |
ml.c4.4xlarge | $0.00 | |
ml.m5.xlarge | $0.00 | |
ml.c5.9xlarge | $0.00 | |
ml.m4.xlarge Vendor Recommended | $0.00 | |
ml.c5.4xlarge | $0.00 | |
ml.p3.8xlarge | $0.00 | |
ml.m5.large | $0.00 | |
ml.c4.8xlarge | $0.00 | |
ml.p2.8xlarge | $0.00 | |
ml.c5.18xlarge | $0.00 |
Usage Information
Model input and output details
Input
Summary
Input Data
Images to Classify mime types:
- application/json
Example:
{'image': '<binary of image>'}
Sample Snippet:
import base64
import json
image_file = 'cat.jpg'
with open(image_file, "rb") as f:
im_bytes = f.read()
im_b64 = base64.b64encode(im_bytes).decode("utf8")
predictor.predict({"image": im_b64})
Input MIME type
application/jsonSample input data
Output
Summary
Image Recognition Output: The top-5 prediction labels of the input image and its probability.
Sample
``` { "tiger cat": 64.559, "tabby, tabby cat": 28.44, "Egyptian cat": 5.997, "lynx, catamount": 0.464, "tiger, Panthera tigris": 0.122 } ``
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
Image Recognition with PyTorch Resnet
Please feel free to reach out to us at: data@rearc.io (or by clicking "Contact Seller")
AWS Infrastructure
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Learn MoreRefund Policy
This product is offered for free. If there are any questions, please contact us for further clarifications.
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