The Auton Lab TA1 primitives
This repository contains additional Auton Lab TA1 primitives for the D3M program.
Iterative Labeling- Blackbox based iterative labeling for semi-supervised learningVideo featurizer- Video Feature Extraction for Action Classification With 3D ResNet
Installation
To install primitives, run:
pip install -U autonbox
Video featurizer requires a static file, for pre-trained model weights.
To download it, run:
mkdir -p /tmp/cmu/pretrained_files
python3 -m d3m index download -o /tmp/cmu/pretrained_files # requires d3m core
Video featurizer
The primitive outputs a data frame of size N x M, where N is the number of videos and M is 2024 features of type float.
It supports running on GPUs.
2 maintainers
Release files for autonbox 0.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| autonbox-0.3.0.tar.gz | 30.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| autonbox-0.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size:66.6 kB
Release files / autonbox-0.3.0.tar.gz
| Download URL | autonbox-0.3.0.tar.gz |
|---|---|
| Size | 30.0 kB |
| Tags | Source |
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Release files / autonbox-0.3.0-py3-none-any.whl
| Download URL | autonbox-0.3.0-py3-none-any.whl |
|---|---|
| Size | 36.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Uploaded via |
twine/4.0.2 CPython/3.8.15
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