Science Score: 26.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (8.6%) to scientific vocabulary
Last synced: 10 months ago
·
JSON representation
Repository
nothing to do
Basic Info
- Host: GitHub
- Owner: R9SEitgb
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Size: 289 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Created over 1 year ago
· Last pushed 10 months ago
Metadata Files
Readme
Changelog
Contributing
Funding
License
Code of conduct
Citation
Codeowners
Security
Roadmap
Authors
Notice
Agents
Dco
Cla
README
To actually get this beast to run on your computer, you'll probably have to
edit some of the matlab files in the config/ subdirectory.
(1) config/datapath.m should return the path where you have your videos stored.
The path should point to a directory which includes a subdirectory for each video.
E.g. $(datapath)/tiger2/.
Each subdirectory should contain files with ground truth labelings and images similar
to what MIL-Track uses.
As an example, see my track-data.zip file.
(2) cluster_ctl is called like so cluster_ctl('on',...) or cluster_ctl('off',...)
to enable or disable the matlab pool. I have three pools, with different characteristics,
available to me so this function, heuristically, selects the best one. Depending on what
type of MATLAB pools you have, you'll need to rewrite this function. The simplest option
would be to call "matlabpool open" when called with "on" and "matlabpool close" when
called with "off".
(3) You should be able to compile by simplying calling "compile.m". I've tested this on Linux
and it should work on other OSs... I welcome patches.
(4) config/cfg.m : lets you stitch many of the parameters of the tracker. Of particular
interest will be the option 'tmp_dir'. You'll want to point this to an empty directory
which is writable. It is used for caching results.
Then, you "should" be able to run it with a command similar to the following:
addpath(genpath('.')); matlab_init('coke11'); track = track_online('coke11');
Alternatively, you might just use track_tbd('coke11'); which doesn't do any learning (after
the first frame) or use a motion model. It is much faster and still performs quite well.
Owner
- Login: R9SEitgb
- Kind: user
- Repositories: 1
- Profile: https://github.com/R9SEitgb
Dependencies
.github/workflows/ci-plus.yml
actions
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Dockerfile
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Cargo.lock
cargo
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Podfile
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mix.exs
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build.gradle
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maven
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pom.xml
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aliyun-nodejs/package.json
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backend/package-lock.json
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backend/package.json
npm
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client/package-lock.json
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client/package.json
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package.json
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FSH.StarterKit.nuspec
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AWS_EC2/requirements.txt
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Pipfile
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requirements.txt
pypi
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setup.py
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Gemfile
rubygems
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Gemfile.lock
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ROS/Dockerfile
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composer.json
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