Feature Extraction
timm
Safetensors
trendyol_arcface
computer-vision
image-feature-extraction
arcface
product-similarity
e-commerce
image-embeddings
convnext
Instructions to use Trendyol/e-commerce-product-image-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use Trendyol/e-commerce-product-image-encoder with timm:
import timm model = timm.create_model("hf-hub:Trendyol/e-commerce-product-image-encoder", pretrained=True) - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -186,10 +186,10 @@ The model has been trained using ArcFace loss which provides several advantages
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```bibtex
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@misc{trendyol2025convnextarcface,
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title={
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author={Trendyol Data Science Team},
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year={2025},
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howpublished={\url{https://huggingface.co/Trendyol/
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}
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```
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- Use the model for non-commercial research and internal testing.
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- Use the model or its derivatives for commercial purposes, provided that:
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- You cite Trendyol as the original model creator.
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- You notify Trendyol in advance via
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### You are not allowed to:
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```bibtex
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@misc{trendyol2025convnextarcface,
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title={E-Commerce Product Image Encoder: High-Fidelity Image Embeddings for E-commerce Product Unification},
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author={Trendyol Data Science Team},
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year={2025},
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howpublished={\url{https://huggingface.co/Trendyol/e-commerce-product-image-encoder }}
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}
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```
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- Use the model for non-commercial research and internal testing.
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- Use the model or its derivatives for commercial purposes, provided that:
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- You cite Trendyol as the original model creator.
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- You notify Trendyol in advance via cqm.datascience@trendyol.com or other designated contact.
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### You are not allowed to:
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