---
license: mit
datasets:
- rmit-denominator/BloomSage-Feature_extractor
language:
- aa
metrics:
- accuracy
library_name: keras
pipeline_tag: image-classification
tags:
- biology
---

## BloomSage Flower Classification and Recommendation Models
- The repository contains 3 flower classification model and 1 feature extractor model for flower recommendation. 
- For more specific instruction, please visit https://github.com/rmit-denominator/bloomsage-ml

### Applications
1. Object recognition 
2. object classification 
3. Building a recommender system 
### Selected models
- For classification, we use the basic structure of Artificial Neutral Network (ANN) and Convolutional Neutral Network (CNN). 
- For feature extractor, we constructed a Convolutional Neural Network (CNN) to extract feature vectors from user preferences image
- Apply a K-Means unsupervised machine learning model to cluster the reference image's feature vector with those of the images in our database. 
### Limitations
- Since our target customer are small flower shops, we just use a sample of 8 flower species with 16362 images.