
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!
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
Details
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This product is offered for free. If there are any questions, please contact us for further clarifications.
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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.
Version release notes
Initial Release
Additional details
Inputs
- 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/json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
image | binary of image | Type: FreeText | Yes |
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Support
Vendor support
Please feel free to reach out to us at: data@rearc.io (or by clicking "Contact Seller")
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