Flower-Image-Classifier is a Machine Learning Project Where it is deployed with the help of streamlit.
This Project classifies which species does the flower belong too and returns the output that what flower species did the model predict. The Models used in this project does give accuracy of more than 90%. The Models/data are still in the development phase where I am constantly collecting the data and trying to learn more about the Neural Networks.
Due to Data & Model Size being Large I am Providing the Google Drive Link so if you want to Have access to the base database and the trained Models you need to get these. Google Drive Link For Trained Models: https://drive.google.com/drive/folders/1NTuaUsqAzz19q2gj9y7WQJKp6DObu9oK?usp=share_link
Google Drive Link For Trained DataSets: https://drive.google.com/drive/folders/1wSxgG_oIBRfUber2ywJFmkt-6lENlCB0?usp=share_link
Steps to Follow to run this project:
i) First of all I reccomend to create an a python environment in your directory by "python -m venv env" ii) Then activate the environment that you have created. iii) Install the requirements.txt file by using "pip install -r requirements.txt" iv) If you have mysql database then run the databasecreation.py file or you need to setup the mysql file. v) Then run main.py by "streamlit run main.py" vi) You can check on your browser the web app has opened and there you can input the 5 classes Images which are present in the dataset.
"The Project will be in development while it can support multiple Inputs at a time and more no.of flower data and much better Machine Learning Model with more time trained and better accuracy."