The AI team at Décathlon Canada developed a library to help with the training of image classification models. It is specially made to exploit the free TPUs that are offered in Google colab notebooks. You can find the full documentation here
Version 1.4.2
Update dependencies and testing functions
Version 1.4.1
Fix hyperparameter optimization for multilabel
Version 1.4.0
Added multilabel classification
Version 1.3.0
Use suggested image sizes Add EfficientNetV2 models
Version 1.2.1
Change to Tensorflow 2.5
Version 1.2.0
Add semi-supervised learning features
Version 1.1.3
Link to public repository
Version 1.1.2
Change name of package
Version 1.1.1
Fix typo in split_train
Version 1.1.0
Remove google scraping
Version 1.0.0
Original release
1 maintainer
Metadata
1 maintainer
Release files for decavision 1.4.2
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| decavision-1.4.2.tar.gz | 30.1 kB | Without provenance | Details for decavision-1.4.2.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| decavision-1.4.2-py3-none-any.whl | Python 3 | none | any | Details for decavision-1.4.2-py3-none-any.whl |
Total release size: 63.2 kB
Release files / decavision-1.4.2.tar.gz
| Download URL | decavision-1.4.2.tar.gz |
|---|---|
| Size | 30.1 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/4.0.1 CPython/3.7.13
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Release files / decavision-1.4.2-py3-none-any.whl
| Download URL | decavision-1.4.2-py3-none-any.whl |
|---|---|
| Size | 33.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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twine/4.0.1 CPython/3.7.13
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