imagecaptioning-aeye
The Image Captioning module for the A-eye project.
Project Organization
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience
Release files for imagecaptioning-aeye 0.1.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| imagecaptioning-aeye-0.1.6.tar.gz | 18.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| imagecaptioning_aeye-0.1.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 65.4 kB
Release files / imagecaptioning-aeye-0.1.6.tar.gz
| Download URL | imagecaptioning-aeye-0.1.6.tar.gz |
|---|---|
| Size | 18.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
a739e174fca344f7ad71d7891cf05535a9f09cc3631543291a7fa467122edc5e
|
|
BLAKE2b-256 checksum How to use checksums |
c29b3f3217edca9123c4d1adcdf557adc2b04a073062a31b99a7c90c63ec5677
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.4.0.post20200518 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.7.7
|
Release files / imagecaptioning_aeye-0.1.6-py3-none-any.whl
| Download URL | imagecaptioning_aeye-0.1.6-py3-none-any.whl |
|---|---|
| Size | 46.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
73cd7e1b573edf9e1f6d8d8e4456a9ca105e0eabf2888b948f8bee775cf26d94
|
|
BLAKE2b-256 checksum How to use checksums |
d1fd770117c5c901ec3f56e17accead33ca071b696845da7a7ee6c96a2326558
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.4.0.post20200518 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.7.7
|