Main idea of QuickCNN is to train deep ConvNet without diving into architectural details. QuickCNN works as an interactive tool for transfer learning, finetuning, and scratch training with custom datasets. It has pretrained model zoo and also works with your custom keras model architecture.
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Release files for quickcnn 0.0.12
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| quickcnn-0.0.12.tar.gz | 27.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| quickcnn-0.0.12-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 53.1 kB
Release files / quickcnn-0.0.12.tar.gz
| Download URL | quickcnn-0.0.12.tar.gz |
|---|---|
| Size | 27.1 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/1.8.0 pkginfo/1.4.2 requests/2.20.1 setuptools/40.6.2 requests-toolbelt/0.8.0 clint/0.5.1 CPython/3.5.2 Linux/4.15.0-42-generic
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Release files / quickcnn-0.0.12-py3-none-any.whl
| Download URL | quickcnn-0.0.12-py3-none-any.whl |
|---|---|
| Size | 26.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.8.0 pkginfo/1.4.2 requests/2.20.1 setuptools/40.6.2 requests-toolbelt/0.8.0 clint/0.5.1 CPython/3.5.2 Linux/4.15.0-42-generic
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