| Name | geode-ml JSON |
| Version |
2.7.1
JSON |
| download |
| home_page | |
| Summary | Classes and methods to help with the creation of geospatial training datasets and deep-learning models. |
| upload_time | 2023-07-14 14:55:23 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) 2022 mpreichenbach Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
deep-learning
training
dataset
geospatial
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
How to install **geode-ml**
====================
The **geode-ml** package depends on **GDAL** and **Tensorflow** for most of its functionality. It is easiest to install
**GDAL** using the **conda** package manager:
```
conda create -n "geode_env" python>=3.7
conda activate geode_env
conda install gdal
```
However, installing **Tensorflow** with Conda is trickier; we recommend following official documentation for installing
the cuDNN and CUDA Toolkit libraries with the **conda** package manager (if you have a compatible GPU), and then doing
```pip install tensorflow-gpu```
After activating an environment which has both **GDAL** and **Tensorflow**, use **pip** to install **geode-ml**:
```
pip install geode-ml
```
The geode.datasets module
-------------------
The datasets module currently contains the class:
1. SemanticSegmentation
* creates and processes pairs of imagery and label rasters for scenes
The geode.losses module
--------------------
The losses module contains custom loss functions for model training; these may be removed in the future when implemented
in Tensorflow.
The geode.models module
--------------------
The models module contains the classes:
1. Segmentation
* subclass of the tensorflow.keras.Model class to be used for image segmentation
2. Unet
* subclass of the Segmentation class which instantiates a Unet architecture.
The geode.utilities module
--------------------
The utilities module currently contains functions to process, single examples of geospatial data. The datasets module
imports these functions to apply to batches of data; however, this module exists so that methods can be used by
themselves, without instantiating a class object from another module.
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"description": "How to install **geode-ml**\r\n====================\r\n\r\nThe **geode-ml** package depends on **GDAL** and **Tensorflow** for most of its functionality. It is easiest to install \r\n**GDAL** using the **conda** package manager:\r\n\r\n```\r\nconda create -n \"geode_env\" python>=3.7\r\nconda activate geode_env\r\nconda install gdal\r\n```\r\n\r\nHowever, installing **Tensorflow** with Conda is trickier; we recommend following official documentation for installing \r\nthe cuDNN and CUDA Toolkit libraries with the **conda** package manager (if you have a compatible GPU), and then doing\r\n\r\n```pip install tensorflow-gpu```\r\n\r\nAfter activating an environment which has both **GDAL** and **Tensorflow**, use **pip** to install **geode-ml**:\r\n\r\n```\r\npip install geode-ml\r\n```\r\n\r\nThe geode.datasets module\r\n-------------------\r\n\r\nThe datasets module currently contains the class:\r\n\r\n1. SemanticSegmentation\r\n\t* creates and processes pairs of imagery and label rasters for scenes\r\n\r\nThe geode.losses module\r\n--------------------\r\n\r\nThe losses module contains custom loss functions for model training; these may be removed in the future when implemented\r\nin Tensorflow.\r\n\r\nThe geode.models module\r\n--------------------\r\n\r\nThe models module contains the classes:\r\n\r\n1. Segmentation\r\n\t* subclass of the tensorflow.keras.Model class to be used for image segmentation\r\n2. Unet\r\n\t* subclass of the Segmentation class which instantiates a Unet architecture.\r\n\r\nThe geode.utilities module\r\n--------------------\r\n\r\nThe utilities module currently contains functions to process, single examples of geospatial data. The datasets module\r\nimports these functions to apply to batches of data; however, this module exists so that methods can be used by \r\nthemselves, without instantiating a class object from another module.\r\n",
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