FLOWER-CLASSIFIER-WEBAPP
This is an neural network webapp visualizing the training of the network and testing accuracy of 98.6% accuracy. The neural network uses pretrained resnet152 and then trained to classify images of flowers. It is built using Pytorch framework using Python as primary language. The webapp is built using flask.
Dataset used :
102 Category Flower Dataset
http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html
Maria-Elena Nilsback and Andrew Zisserman
Neural Network used :
Resnet152
Network Layers in Pytorch
Refresher on Neural Network :
Gradient Descent
Backpropogation
Flow :
- Input image is fed and transformed using : commons.py
- Inference is done by : inference.py
Complete flow :
Run on windows -
Make sure you have installed Python , Pytorch and flask.
- First download all the folders and files
git clone https://github.com/souravs17031999/FLOWER-CLASSIFIER-WEBAPP.git - Download pretrained weights and keep it in the same Project directory Download here.
- Then open the command prompt (or powershell) and change the directory to the path where all the files are located.
cd FLOWER-CLASSIFIER-WEBAPP - Now run the following commands -
set FLASK_APP=flower.py
flask run
This will firstly download the models and then start the local web server.
now go to the local server something like this - http://127.0.0.1:5000/ and see the result and explore.
@creator - sourav kumar