Initial lib was developed during Open AI Challenge with Omdena. Learn more about the challenge at here
This is a fork from the original (and on-going) HOT project, created with the aim to analyse its performance and to work on improving it.
The workflow and notes behind this project is in this HackMD (ask for access if needed).
Note:
- write about the
env
file - write about the metric generation
hot_fair_utilities is a collection of utilities which contains core logic for model data preparation, training and postprocessing. It can support multiple models, currently ramp is supported.
-
To get started clone this repo first :
git clone https://github.com/hotosm/fAIr-utilities.git
-
Setup your virtualenv with
python 3.8
( Ramp is tested with python 3.8 ) -
Install tensorflow
2.9.2
from [here] (https://www.tensorflow.org/install/pip) According to your os
-
Copy your basemodel : Basemodel is derived from ramp basemodel
git clone https://github.com/radiantearth/model_ramp_baseline.git
-
Clone ramp working dir
git clone https://github.com/kshitijrajsharma/ramp-code-fAIr.git ramp-code
-
Copy base model to ramp-code
cp -r model_ramp_baseline/data/input/checkpoint.tf ramp-code/ramp/checkpoint.tf
-
Install native bindings
-
Install Numpy
pip install numpy==1.23.5
-
Install gdal .
for eg : on Ubuntu
sudo add-apt-repository ppa:ubuntugis/ppa && sudo apt-get update sudo apt-get install gdal-bin sudo apt-get install libgdal-dev export CPLUS_INCLUDE_PATH=/usr/include/gdal export C_INCLUDE_PATH=/usr/include/gdal pip install --global-option=build_ext --global-option="-I/usr/include/gdal" GDAL==`gdal-config --version`
on conda :
conda install -c conda-forge gdal
-
Install rasterio
for eg: on ubuntu :
sudo apt-get install -y python3-rasterio
on conda :
conda install -c conda-forge rasterio
-
-
Install ramp requirements
Install necessary requirements for ramp and hot_fair_utilites
cd ramp-code && cd colab && make install && cd ../.. && pip install -e .
Create from env fle
conda env create -f environment.yml
Create your own
conda create -n fAIr python=3.8
conda activate fAIr
conda install -c conda-forge gdal
conda install -c conda-forge geopandas
pip install pyogrio rasterio tensorflow
pip install -e hot_fair_utilities
You can run package_test.ipynb
to your pc to test the installation and workflow with sample data provided