image-similarity-using-deep-ranking | Learning Fine-grained Image Similarity | Machine Learning library
kandi X-RAY | image-similarity-using-deep-ranking Summary
kandi X-RAY | image-similarity-using-deep-ranking Summary
image-similarity-using-deep-ranking is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. image-similarity-using-deep-ranking has no bugs, it has no vulnerabilities and it has low support. However image-similarity-using-deep-ranking build file is not available. You can download it from GitHub.
You will design a simplified version of the deep ranking model as discussed in the paper. Your network architecture will look exactly the same, but the details of the triplet sampling layer will be a lot simpler. The architecture consists of $3$ identical networks $(Q,P,N)$. Each of these networks take a single image denoted by $p_i$ , $p_i^+$ , $p_i^-$ respectively. The output of each network, denoted by $f(p_i)$, $f(p_i^+)$, $f(p_i^-)$ is the feature embedding of an image. This gets fed to the ranking layer.
You will design a simplified version of the deep ranking model as discussed in the paper. Your network architecture will look exactly the same, but the details of the triplet sampling layer will be a lot simpler. The architecture consists of $3$ identical networks $(Q,P,N)$. Each of these networks take a single image denoted by $p_i$ , $p_i^+$ , $p_i^-$ respectively. The output of each network, denoted by $f(p_i)$, $f(p_i^+)$, $f(p_i^-)$ is the feature embedding of an image. This gets fed to the ranking layer.
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image-similarity-using-deep-ranking has a low active ecosystem.
It has 137 star(s) with 33 fork(s). There are 3 watchers for this library.
It had no major release in the last 6 months.
There are 1 open issues and 10 have been closed. On average issues are closed in 588 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of image-similarity-using-deep-ranking is current.
Quality
image-similarity-using-deep-ranking has 0 bugs and 0 code smells.
Security
image-similarity-using-deep-ranking has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
image-similarity-using-deep-ranking code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
image-similarity-using-deep-ranking does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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image-similarity-using-deep-ranking releases are not available. You will need to build from source code and install.
image-similarity-using-deep-ranking has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions are not available. Examples and code snippets are available.
image-similarity-using-deep-ranking saves you 259 person hours of effort in developing the same functionality from scratch.
It has 628 lines of code, 37 functions and 9 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed image-similarity-using-deep-ranking and discovered the below as its top functions. This is intended to give you an instant insight into image-similarity-using-deep-ranking implemented functionality, and help decide if they suit your requirements.
- Calculate accuracy
- Return a TripletNet object
- Returns a dictionary of the class attributes
- Create a resnet
- Generate an embedding
- Predict the predictions from a test image
- Transforms an image
- Train the NearestNeighbors model
- Plots image search results
- Generate a triplet dataset
- List all images in a directory
- Returns a list of positive images from the given image_name
- Returns a list of negative images
- Train the network
- Save checkpoint
- Load a TinyImageNet dataset
- Generate the mean and standard deviation
- Load all training images
- Creates an embedding network
- Load training embedding
Get all kandi verified functions for this library.
image-similarity-using-deep-ranking Key Features
No Key Features are available at this moment for image-similarity-using-deep-ranking.
image-similarity-using-deep-ranking Examples and Code Snippets
No Code Snippets are available at this moment for image-similarity-using-deep-ranking.
Community Discussions
Trending Discussions on image-similarity-using-deep-ranking
QUESTION
Keras image similarity model trouble with labels
Asked 2019-Dec-01 at 14:01
I am working on a deep image similarity model and I would like to get some help on it.
I keep getting this error and don't know what to exactly do with it or how to fix it.
...ANSWER
Answered 2019-Jun-28 at 04:27As stated in the error, the input array [a,p,n] is of size(100x3) but your output array y is of size (1x3). So the model is not able to pair the input array to its corresponding output.
From your explanation, I understand that a -> 1, p -> 1, and n -> 0, and you have 100 samples in each category. So you just need to multiply the output array by 100. Try this:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install image-similarity-using-deep-ranking
You can download it from GitHub.
You can use image-similarity-using-deep-ranking like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use image-similarity-using-deep-ranking like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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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