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metadata
license: mit
library_name: pytorch
tags:
- image-retrieval
- metric-learning
- siamese-network
- furniture
- compatibility
- triplet-loss
pipeline_tag: image-to-image
Furniture Compatibility Siamese Network
Models and retrieval artifacts from the bachelor's thesis "Visual Furnishings Compatibility Learning and Retrieval Using Machine Learning" (Ukrainian Catholic University, 2026).
Architecture
Siamese ResNet18 · embedding head: Linear(512→256) → BN → ReLU → Linear(256→128) · L2-normalised output · triplet margin loss (margin=1.0) · AdamW + linear warmup + ReduceLROnPlateau · two separate models (bedrooms / living rooms).
Repo layout
bedrooms/
best_model.pt — PyTorch checkpoint (weights + config dict)
retrieval_embeddings.npz — 128-d embeddings for all catalog items
retrieval_index.json — row → furniture metadata
retrieval_histograms_bc.npz — sqrt-encoded RGB histograms (96-d)
living_rooms/
(same four files)
Quick start (run the app)
git clone https://github.com/Baredal/thesis_furnitures
cd thesis_furnitures
pip install -r requirements.txt
git lfs install
python download_from_hf.py # downloads this repo + the image catalog
streamlit run src/app/streamlit_app.py
Load a checkpoint
import torch
from src.ml.model import SiameseResnet18
from huggingface_hub import hf_hub_download
ckpt_path = hf_hub_download("Darebal/furniture-compatibility-siamese",
"bedrooms/best_model.pt")
ckpt = torch.load(ckpt_path, map_location="cpu")
model = SiameseResnet18(embedding_dim=ckpt["config"]["embedding_dim"])
model.load_state_dict(ckpt["model_state_dict"])
model.eval()
Related
- Image catalog + triplets: Darebal/furniture-catalog
- Code: github.com/Baredal/thesis_furnitures
Citation
@thesis{Strus2026furniture,
title = {Visual Furnishings Compatibility Learning and Retrieval Using Machine Learning},
author = {Yaroslav-Dmytro Strus},
school = {Ukrainian Catholic University},
year = {2026},
type = {Bachelor's Thesis}
}
License
Model weights: MIT.
Training images: not redistributed — sourced from DeepFurniture and Sklad Mebliv for non-commercial academic research only.