Feature Extraction
Transformers
PyTorch
Safetensors
English
trendyol_dinov2
image-feature-extraction
image-similarity
image-retrieval
computer-vision
e-commerce
dinov2
custom_code
Eval Results (legacy)
Instructions to use Trendyol/trendyol-dino-v2-ecommerce-256d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Trendyol/trendyol-dino-v2-ecommerce-256d with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Trendyol/trendyol-dino-v2-ecommerce-256d", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Trendyol/trendyol-dino-v2-ecommerce-256d", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
feat: test upload - Trendyol DinoV2 Product Similarity and Retrieval Embedding Model
Browse files🧪 Test Upload Details:
- Personal account testing before company publication
- Architecture: ConvNeXt-Base + ArcFace loss
- Embedding dimension: 256
- Task: Product similarity and retrieval
📁 Repository Contents:
- Model weights in safetensors format
- Complete model card with usage examples
- Demo notebook for inference
🔒 Security: Scanned and validated
📋 RFC Compliance: Ready for company publication
Test upload by: Personal Account
README.md
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@@ -150,7 +150,7 @@ By downloading or using this model, you agree to the terms above.
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```
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@misc{trendyol-dinov2-ecommerce,
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title={Trendyol DinoV2 E-commerce Image Similarity Model},
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author={Trendyol
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year={2025},
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url={https://huggingface.co/Trendyol/trendyol-dino-v2-ecommerce-256d}
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}
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```
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@misc{trendyol-dinov2-ecommerce,
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title={Trendyol DinoV2 E-commerce Image Similarity Model},
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author={Trendyol Data Science Team},
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year={2025},
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url={https://huggingface.co/Trendyol/trendyol-dino-v2-ecommerce-256d}
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}
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__pycache__/modeling_trendyol_dinov2.cpython-312.pyc
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