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Merge pull request #31 from DeepLabCut/v0.3
v0.3
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README.md

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This package contains a [DeepLabCut](http://www.mousemotorlab.org/deeplabcut) inference pipeline for real-time applications that has minimal (software) dependencies. Thus, it is as easy to install as possible (in particular, on atypical systems like [NVIDIA Jetson boards](https://developer.nvidia.com/buy-jetson)).
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**Performance:** If you would like to see estimates on how your model might perform given a video size, neural network type, and hardware, please see: https://deeplabcut.github.io/DLC-inferencespeed-benchmark/ And, consider submitting your results too! https://github.com/DeepLabCut/DLC-inferencespeed-benchmark
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**Performance:** If you would like to see estimates on how your model should perform given different video sizes, neural network type, and hardware, please see: https://deeplabcut.github.io/DLC-inferencespeed-benchmark/
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If you have different hardware, please consider submitting your results too! https://github.com/DeepLabCut/DLC-inferencespeed-benchmark
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**What this SDK provides:** This package provides a `DLCLive` class which enables pose estimation online to provide feedback. This object loads and prepares a DeepLabCut network for inference, and will return the predicted pose for single images.
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benchmarking/run.py

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dlclive/version.py

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"""
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__version__ = "0.0.2"
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__version__ = "0.0.3"
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VERSION = __version__

reinstall.sh

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pip uninstall deeplabcut-live
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python3 setup.py sdist bdist_wheel
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pip install dist/deeplabcut_live-0.0.2-py3-none-any.whl
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pip install dist/deeplabcut_live-0.0.3-py3-none-any.whl

setup.py

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setuptools.setup(
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name="deeplabcut-live",
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version="0.0.2",
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version="0.0.3",
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author="A. & M. Mathis Labs",
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author_email="[email protected]",
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description="Class to load exported DeepLabCut networks and perform pose estimation on single frames (from a camera feed)",

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