spatial-transformer-network | Tensorflow implementation of Spatial Transformer | Machine Learning library
kandi X-RAY | spatial-transformer-network Summary
kandi X-RAY | spatial-transformer-network Summary
spatial-transformer-network is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Transformer applications. spatial-transformer-network has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install spatial-transformer-network' or download it from GitHub, PyPI.
This is a Tensorflow implementation of Spatial Transformer Networks by Max Jaderberg, Karen Simonyan, Andrew Zisserman and Koray Kavukcuoglu, accompanying by two-part blog tutorial series. Spatial Transformer Networks (STN) is a differentiable module that can be inserted anywhere in ConvNet architecture to increase its geometric invariance. It effectively gives the network the ability to spatially transform feature maps at no extra data or supervision cost.
This is a Tensorflow implementation of Spatial Transformer Networks by Max Jaderberg, Karen Simonyan, Andrew Zisserman and Koray Kavukcuoglu, accompanying by two-part blog tutorial series. Spatial Transformer Networks (STN) is a differentiable module that can be inserted anywhere in ConvNet architecture to increase its geometric invariance. It effectively gives the network the ability to spatially transform feature maps at no extra data or supervision cost.
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Quality
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spatial-transformer-network has a highly active ecosystem.
It has 824 star(s) with 238 fork(s). There are 20 watchers for this library.
It had no major release in the last 6 months.
There are 18 open issues and 9 have been closed. On average issues are closed in 37 days. There are 1 open pull requests and 0 closed requests.
It has a positive sentiment in the developer community.
The latest version of spatial-transformer-network is current.
Quality
spatial-transformer-network has 0 bugs and 0 code smells.
Security
spatial-transformer-network has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
spatial-transformer-network code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
spatial-transformer-network is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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spatial-transformer-network releases are not available. You will need to build from source code and install.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
spatial-transformer-network saves you 45 person hours of effort in developing the same functionality from scratch.
It has 121 lines of code, 7 functions and 4 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed spatial-transformer-network and discovered the below as its top functions. This is intended to give you an instant insight into spatial-transformer-network implemented functionality, and help decide if they suit your requirements.
- Creates a spatial transformer network
- Calculate bilinear sampling
- Generate a 2D grid
- Get pixel value
Get all kandi verified functions for this library.
spatial-transformer-network Key Features
No Key Features are available at this moment for spatial-transformer-network.
spatial-transformer-network Examples and Code Snippets
No Code Snippets are available at this moment for spatial-transformer-network.
Community Discussions
Trending Discussions on spatial-transformer-network
QUESTION
Obtaining output of an Intermediate layer in TensorFlow/Keras
Asked 2017-Apr-17 at 21:11
I'm trying to obtain output of an intermediate layer in Keras, Following is my code:
...ANSWER
Answered 2017-Apr-17 at 21:11The easiest way is to create a new model in Keras, without calling the backend. You'll need the functional model API for this:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install spatial-transformer-network
Install the stn package using:.
input_feature_map: the output of the layer preceding the localization network. If the STN layer is the first layer of the network, then this corresponds to the input images. Shape should be (B, H, W, C).
theta: this is the output of the localization network. Shape should be (B, 6)
out_dims: desired (H, W) of the output feature map. Useful for upsampling or downsampling. If not specified, then output dimensions will be equal to input_feature_map dimensions.
input_feature_map: the output of the layer preceding the localization network. If the STN layer is the first layer of the network, then this corresponds to the input images. Shape should be (B, H, W, C).
theta: this is the output of the localization network. Shape should be (B, 6)
out_dims: desired (H, W) of the output feature map. Useful for upsampling or downsampling. If not specified, then output dimensions will be equal to input_feature_map dimensions.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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