Skip to content

Latest commit

 

History

History
67 lines (46 loc) · 1.72 KB

README.md

File metadata and controls

67 lines (46 loc) · 1.72 KB
Raw

fAIr Predictor

Run your fAIr Model Predictions anywhere !

Prerequisites

fAIr Predictor has support for GPU , CPU and tflite based devices

  • Install tensorflow-cpu or tflite-runtime according to your requirements

tflite-runtime support is for having very light deployment in order to run inference & tensorflow-cpu might require installation of efficientnet

Example on Collab

# Install 
!pip install fairpredictor

# Import 
from predictor import predict

# Parameters for your predictions 
bbox=[100.56228021333352,13.685230854641182,100.56383321235313,13.685961853747969]
model_path='checkpoint.h5'
zoom_level=20
tms_url='https://tiles.openaerialmap.org/6501a65c0906de000167e64d/0/6501a65c0906de000167e64e/{z}/{x}/{y}'

# Run your prediction 
my_predictions=predict(bbox,model_path,zoom_level,tms_url)
print(my_predictions)

## Visualize your predictions 

import geopandas as gpd
import matplotlib.pyplot as plt
gdf = gpd.GeoDataFrame.from_features(my_predictions)
gdf.plot()
plt.show()

Works on CPU ! Can work on serverless functions, No other dependencies to run predictions

Use raster2polygon

There is another postprocessing option that supports distance threshold between polygon for merging them , If it is useful for you install raster2polygon by :

pip install raster2polygon

Load Testing

CAUTION : Always take permission of server admin before you perform load test

In order to perform load testing we use Locust , To enable this hit following command within the root dir

  • Install locust

    pip install locust

  • Run locust script

    locust -f locust.py

Populate your HOST and replace it with BASE URL of the Predictor URL