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This is a Image Classification model from PyTorch Hub
Overview
This is an Image Classification model from PyTorch Hub . It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.
Highlights
- This is an Image Classification model from PyTorch Hub: https://pytorch.org/hub/pytorch_vision_squeezenet/
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Deployed on AWS
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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
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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/pytorch-metadata/assets/cat.jpg
https://jumpstart-cache-prod-us-west-2.s3-us-west-2.amazonaws.com/pytorch-metadata/assets/cat.jpg
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This is a Image Classification model from PyTorch Hub
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1 external reviews
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Computer Software
Must try model for CV tasks
Reviewed on Sep 05, 2023
Review provided by G2
What do you like best about the product?
SqueezeNet is comparatively small compared to VGG and other ResNet-based architectures
The architecture is designed in such a way that it doesn't compromise and performance with less parameteres
The architecture is designed in such a way that it doesn't compromise and performance with less parameteres
What do you dislike about the product?
SqueezeNet performs well when the dataset is limited. Training with lots of data can reduce the performance of the neural net since the architecture is quite small
What problems is the product solving and how is that benefiting you?
SqueezeNet can be used in mobile applications that involves computer vision tasks since the model is small and easy to train, evalulate and deploy.
Activation layers can be experimented to improve performance
Activation layers can be experimented to improve performance