A simple fashion recommendation system that can recommend the users items similar to something that they like. A user can input a clothing image and he will get several similar looking recommendations.
The tech-stack used:
Frontend: Flutter and dart
Backend: Django and python
├── backend
│────├─ backend
│────├─ model
│────├─ manage.py
│
├── frontend
│
├── models
│────├── detector
│──────────└── dataset
│───────────────└── images
│───────────────└── styles.csv
│
├── config.py
│
├── controller.py
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Data-Collection: A kaggle dataset was used, it consists of about 44000 images spanning across 140 categories of clothing.
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Data-Preprocessing:
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Added a new column which consisted of image_path (pointed to the images folder) for each product_id.
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Images were converted into feature vectors using a pre-trained vgg16 model (with output layer removed)
- Prediction:
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For a new input image, first it gets pre-processed and then cosine similarities are calculated for the new image feature vector and the remaining image vectors (that belong to the new image clothing category)
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N most similar items are the output