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This is a Image Classification model from PyTorch Hub
3.3
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/
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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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Customer reviews
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.