DataJoint Element for Pose Estimation with DeepLabCut
DataJoint Element for markerless pose estimation with DeepLabCut. DataJoint Elements collectively standardize and automate data collection and analysis for neuroscience experiments. Each Element is a modular pipeline for data storage and processing with corresponding database tables that can be combined with other Elements to assemble a fully functional pipeline.
Experiment Flowchart
Data Pipeline Diagram
Getting Started
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Install from PyPI
pip install element-deeplabcut
Support
- If you need help getting started or run into any errors, please open a GitHub Issue or contact our team by email at support@datajoint.com.
1 maintainer
Release files for element-deeplabcut 0.2.8
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| element-deeplabcut-0.2.8.tar.gz | 20.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| element_deeplabcut-0.2.8-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 43.3 kB
Release files / element-deeplabcut-0.2.8.tar.gz
| Download URL | element-deeplabcut-0.2.8.tar.gz |
|---|---|
| Size | 20.8 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/4.0.2 CPython/3.9.17
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Release files / element_deeplabcut-0.2.8-py3-none-any.whl
| Download URL | element_deeplabcut-0.2.8-py3-none-any.whl |
|---|---|
| Size | 22.5 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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No |
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twine/4.0.2 CPython/3.9.17
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