from huggingface_hub import hf_hub_download
import torch

# Download models
resnet_path = hf_hub_download(repo_id="Stylique/dressify-models", filename="resnet_item_embedder_best.pth")
vit_path = hf_hub_download(repo_id="Stylique/dressify-models", filename="vit_outfit_model_best.pth")

# Load models
resnet_model = torch.load(resnet_path)
vit_model = torch.load(vit_path)

Training Details

Architecture: ResNet50 with custom projection head
Purpose: Generate 512-dimensional embeddings for fashion items
Training: Triplet loss with semi-hard negative mining
Input: Fashion item images (224x224)
Output: L2-normalized 512D embeddings

Dataset: Polyvore Outfits (Stylique/Polyvore)
Loss: Triplet margin loss
Optimizer: AdamW
Mixed Precision: Enabled
Hardware: NVIDIA GPU with CUDA

Performance

ResNet: ~25M parameters, fast inference
ViT: ~12M parameters, efficient outfit scoring
Memory: Optimized for deployment on Hugging Face Spaces

Citation

If you use these models in your research, please cite:

bibtex

@misc{dressify2024,
  title={Dressify: Deep Learning for Fashion Outfit Recommendation},
  author={Stylique},
  year={2024},
  url={https://huggingface.co/Stylique/dressify-models}
}