Sold by: Amazon Web ServicesÂ
Deployed on AWS
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_densenet/
Details
Sold by
Categories
Delivery method
Latest version
Deployed on AWS
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
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 |
Vendor refund policy
None
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
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
Resources
Support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
This is a Image Classification model from PyTorch Hub
This is a Image Classification model from PyTorch Hub
This is a Image Classification model from PyTorch Hub
Customer reviews
0 ratings
0 AWS reviews
|
2 external reviews
Star ratings include only reviews from verified AWS customers. External reviews can also include a star rating, but star ratings from external reviews are not averaged in with the AWS customer star ratings.
Emanuel C.
Powerhouse for Image Recognition
Reviewed on Mar 04, 2024
Review provided by G2
What do you like best about the product?
DenseNet 161 has outperformed many other image recognition tasks, and it continues to present highly distinguishing results. It provides diminishing feature map reuse tips in Layer, which makes it both timesaving and effective for the classification of complicated image design.
What do you dislike about the product?
DenseNet 161 regulation of memory remark as its negative. The layered density's memory consumption also renders it a resource-deficient alternative for spaces that are limited in terms of resources and for datasets that are small in size.
What problems is the product solving and how is that benefiting you?
For projects that I am doing for deep learning involving image recognition, DenseNet-161 is a tool of choice. The accuracy it delivers along with efficiency have helped me to attain good score times while for the most part the workout sessions are made bearable. Nevertheless, to make use of this powerful tool, I will need to think over the memory implications for the jobs with limited resource.
Moreira Q.
Amazing powerhouse for Picture Affirmation
Reviewed on Mar 02, 2024
Review provided by G2
What do you like best about the product?
DenseNet-161 shines in its ability to manage complex picture affirmation tasks. It prevails at getting convoluted nuances and associations inside an image, provoking incredibly precise portrayals. This makes it a necessary resource for projects like thing area and scene understanding.
What do you dislike about the product?
The complexity of DenseNet-161 goes with a split the difference. It requires a ton of computational power and planning data to show up at its greatest limit. This can be a snag for those with confined resources or more humble datasets.
What problems is the product solving and how is that benefiting you?
DenseNet-161 has been an asset for my work in picture gathering. Its ability to eliminate nuanced nuances has generally dealt with the accuracy of my models. Regardless, I've expected to carefully manage the computational resources expected to plan and run it as a matter of fact.