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https://github.com/pranay-pandey/fashionmodule
https://github.com/pranay-pandey/fashionmodule
fashion feature-extraction graph-database machine-learning neo4j ontology
Last synced: about 9 hours ago
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- Host: GitHub
- URL: https://github.com/pranay-pandey/fashionmodule
- Owner: Pranay-Pandey
- Created: 2025-01-05T09:49:44.000Z (8 days ago)
- Default Branch: ml_models
- Last Pushed: 2025-01-12T17:14:07.000Z (1 day ago)
- Last Synced: 2025-01-12T18:22:57.453Z (1 day ago)
- Topics: fashion, feature-extraction, graph-database, machine-learning, neo4j, ontology
- Language: Jupyter Notebook
- Homepage: https://stylumia-fashion.streamlit.app/
- Size: 80.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# stylumia-nxt
Our solution for the hackathon
MODEL BUILDING:
TEXT2TEXT MDOEL : DESCRIPTION+FEATURE_MAP+.. => STYLE_ATTRIBUTES
IMAGE2TEXT MDOEL : IMAGE OF THE PRODUCT => STYLE_ATTRIBUTES
ATTRIBUTE SELECTION MODEL/ONTOLOGY CREATION MDOEL : STYLE_ATTRIBUTES => ONTOLOGY MAPPING (REQURE THE NEO4J INTEGRATION SUPPORT)
NEO4J BUILDING:
CREATE AN ONTLOGY MAP/GRAPH IN THE NEO4J : MANNUAL UPDATION
CREATE A DATASET IN THE NEO4J : AUTOMATED CREATION
CREATE A QUERY / AI SIMULATION ENGINE : RUN QUERIES
INSTAGRAM CELEBERTIES EXTRACTION ENGINE:
MAKE LIST OF 100 CELEBS
EXTRACT THE IMAGE FROM IG POST OF THOSE CELEBS
ANALYSE THE CLOTHING
MAP THAT CLOTHING STYLE TO THE ONTOLOGY -> UPDATE THE ONTOLOGY (SUGGESTIONS ONLY)
TREND ANALYSIS ENGINE:
COMMENT SCRAPE :SCRAP THE COMMENTS AND FIND THE SENTIMENT
FREQUENCY ANALYSIS : BASED ON THE RECENT SUGGESTION IN THE ONTOLOGY
INTEGRATION:
NA
Bathroom -> Features list all good, Attributes all good
Shirts -> Features list all null, Attributes all good
Tshirts -> Features list all null, Attributes all good
Watches -> Features list 80%good, Attributes all good
Saree -> Features list 5% good, Attributes 10% good
Kurtis -> Features list 0% good, Attributes 0% good
Jeans -> Features list all null, Attributes all good
Earring -> Features 95%good, Attributes 95% good
Dresses -> Features all good
Sneakers -> Features list all null, Attributes all good
Fashion Ontology
1. Garment
• Category
• Jeans
• Shirts
• Tops
• Skirts
• Outerwear
• Pants
2. Attributes
• General Properties
• Color
• Size
• Style
• Pattern Type
• Fit Type
• Concept
• Features
• Material Properties
• Material
• Composition
• Fabric
• Lining
• Coating
• Design & Construction
• Length
• Waist Line
• Closure Type
• Neckline
• Sleeve Length
• Sleeve Type
• Hem Shaped
• Pockets
• Belt
• Placket
• Special Design
• Jeans Style
• Bottom Type
• Waist Rise
• Detailed Description
• Special Features
3. Functional Attributes
• Care Instructions
• Lined for Added Warmth
• Sheer
• Temperature Suitability
• Number of Pieces
• Chest Pad
4. Contextual Properties
• Imported
• Body Type
• Concept
Product Name (=Node)
Properties:
k,v (value=Node) (key=relation)