ContrastLandmark | Unsupervised object landmark discovery

by cvl-umass Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | ContrastLandmark Summary

ContrastLandmark is a Python library. ContrastLandmark has no bugs, it has no vulnerabilities and it has low support. However ContrastLandmark build file is not available. You can download it from GitHub.

Unsupervised object landmark discovery

Support

Quality

Security

License

Reuse

Support

ContrastLandmark has a low active ecosystem.

It has 17 star(s) with 2 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

ContrastLandmark has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ContrastLandmark is current.

Quality

ContrastLandmark has 0 bugs and 0 code smells.

Security

ContrastLandmark has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ContrastLandmark code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ContrastLandmark 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.

Reuse

ContrastLandmark releases are not available. You will need to build from source code and install.

ContrastLandmark has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

ContrastLandmark saves you 1856 person hours of effort in developing the same functionality from scratch.

It has 4095 lines of code, 215 functions and 25 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed ContrastLandmark and discovered the below as its top functions. This is intended to give you an instant insight into ContrastLandmark implemented functionality, and help decide if they suit your requirements.

Main function .
Parse command line options
Get an image by index .
Convert a 2D tensor .
Train the MOCO model .
This function is used to create keypoints .
Train model .
This function generates a random keypoint .
forward computation .
Compute PCA layer .

Get all kandi verified functions for this library.

ContrastLandmark Key Features

No Key Features are available at this moment for ContrastLandmark.

ContrastLandmark Examples and Code Snippets

No Code Snippets are available at this moment for ContrastLandmark.

Community Discussions

No Community Discussions are available at this moment for ContrastLandmark.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ContrastLandmark

The implementation is based on DVE [Thewlis et al. ICCV 2019] and CMC [Tian et al. 2019]. (Dependencies: tensorboard-logger, pytorch=1.4.0, torchfile).
Note: On face benchmarks, the numbers in Table 1 in the main text are reported at 120th, 45th, 80th epoch for MAFL, AFLW and 300W. The epoch is indexing from 0. However, the index was starting from 1 when we saved the model. This leads to different scores with the saved model from these in Table 1 (either slightly better or slightly worse).
Contrastively learning models:
Celeb: [MoCo-ResNet18-CelebA] [MoCo-ResNet50-CelebA]
iNat Aves: [MoCo-ResNet18-iNat] [MoCo-ResNet50-iNat] [DVE-Hourglass-iNat]
Linear-regressor: [Face benchmarks] [Bird benchmarks]

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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