Visual Docker Container Management on Mac & Windows
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Jun 11, 2021 - JavaScript
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toolbox
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More custom services
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bastienwirtz
commented
Oct 6, 2021
Homer supports custom services, and provides a template to easily add new ones.
It could be great to add some more. Before starting to work on a custom service please open an issue to describe your idea and discuss about it.
Some ide
Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
deep-learning
toolbox
behavior-analysis
pose-estimation
labeling-tool
feature-detectors
keypoint-tracking
deeplabcut
keypoint-detection
animal-pose-estimation
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Oct 19, 2021 - Python
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
python
machine-learning
reinforcement-learning
robotics
pytorch
toolbox
openai
gym
reinforcement-learning-algorithms
sde
baselines
stable-baselines
sb3
gsde
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Oct 18, 2021 - Python
SOTA Re-identification Methods and Toolbox
computer-vision
re-ranking
pytorch
toolbox
apex
baseline
image-search
image-retrieval
re-identification
person-reidentification
reids
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open-reid
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Oct 8, 2021 - Python
All basic TypeScript types in one place
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Oct 16, 2021 - TypeScript
PWA with 12 open source frontend focused tools
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May 24, 2021 - JavaScript
A python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
visualization
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bayesian-neural-networks
bayesian-deep-learning
sharpness
predictive-uncertainty
recalibration
uncertainty-calibration
uncertainty-metrics
uncertainty-toolbox
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Sep 22, 2021 - Python
Open source deep learning based unsupervised image retrieval toolbox built on PyTorch
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Jan 25, 2021 - Python
A Toolbox for Adversarial Robustness Research
security
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robustness
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adversarial-example
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Aug 26, 2021 - Jupyter Notebook
A toolbelt of useful classes and functions to be used with python-requests
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Oct 13, 2021 - Python
Jok3r v3 BETA 2 - Network and Web Pentest Automation Framework
docker
cli
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security-audit
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framework
network
scanner
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nmap
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scan-tool
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vulnerability-scanners
metasploit
msfconsole
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toolbox-management
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Aug 1, 2021 - HTML
ALiPy: Active Learning in Python is an active learning python toolbox, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods.
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Oct 8, 2021 - Python
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
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machine-learning
algorithm
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causality
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Jul 16, 2021 - Python
Tool-box for efficient build and debug in Python. Especially for Scientific Computing and Computer Vision.
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Oct 12, 2021 - Python
A General Toolbox for Identifying Object Detection Errors
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Oct 15, 2021 - Python
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Oct 16, 2021 - Vue
A single stage object detection toolbox based on PyTorch
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Sep 7, 2021 - Python
[Active development]ToolBox to make using Pytorch much easier.Give it a star if you feel helpful.
metrics
lmdb
initializers
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swish
cutout
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lookahead
mixup
flops
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labelsmoothing
cosinewarmuplr
switchnorm
arcloss
cosloss
l2softmax
teansform
lmdb-dataset
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Aug 22, 2021 - Python
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May 10, 2021 - Python
AnderBiguri
commented
Sep 23, 2021
As the title says, this would be a parameter that allows the quality measurement parameters to be computed against a known image, rather than change per iteration.
Something like:
[img, qual] = SIRT(proj,geo,angles, 'QualMeas','RMSE','ground_truth',my_image)
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Sep 12, 2021 - Kotlin
carterbox
commented
Jun 11, 2019
According to the docs, filters are not implemented for FBP. However, TomoPy does not raise an error when you try to pass filter_name or filter_par with algorithm='fbp'. This would explain why, in my tests, choosing various filters for FBP did not change the reconstruction quality.
This definitely needs to be fixed
PyTorch based Deep Learning Toolbox
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Jul 27, 2018 - Python
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The following applies to DDPG and TD3, and possibly other models. The following libraries were installed in a virtual environment:
numpy==1.16.4
stable-baselines==2.10.0
gym==0.14.0
tensorflow==1.14.0
Episode rewards do not seem to be updated in
model.learn()beforecallback.on_step(). Depending on whichcallback.localsvariable is used, this means that: