---
language: en
license: mit
tags:
- computer-vision
- fashion
- outfit-recommendation
- deep-learning
- resnet
- vision-transformer
---

# Dressify Outfit Recommendation Models

This repository contains the trained models for the Dressify outfit recommendation system.

## Models

### ResNet Item Embedder
- **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

### ViT Outfit Compatibility Model
- **Architecture**: Vision Transformer encoder
- **Purpose**: Score outfit compatibility from item embeddings
- **Training**: Triplet loss with cosine distance
- **Input**: Variable-length sequence of item embeddings
- **Output**: Compatibility score (0-1)

## Usage

```python
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

- **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}
}
```