rmit-denominator/bloomsage

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

2. Flower classification
3. Recommender system

Selected models

• For classification, we use the basic structure of Artificial Neutral Network (ANN) and Convolutional Neutral Network (CNN).
• For the 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 customers are small flower shops, we just use a sample of 8 flower species with 16362 images.

How to use :

• Dependencies :
• huggingface-hub,
• gitlfs