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README.md

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+---
+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}
+}
+```