Feature Extraction
Transformers
PyTorch
Safetensors
English
trendyol_dinov2
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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: test upload - Trendyol DinoV2 Product Similarity and Retrieval Embedding Model
a15fec5 verified | """ | |
| Trendyol DinoV2 Image Similarity Model | |
| This package contains a fine-tuned DinoV2 model for e-commerce image similarity. | |
| Fully compatible with Hugging Face transformers. | |
| """ | |
| from .modeling_trendyol_dinov2 import TrendyolDinoV2Model, TrendyolDinoV2Config | |
| from .image_processing_trendyol_dinov2 import TrendyolDinoV2ImageProcessor | |
| # Register for AutoModel and AutoImageProcessor | |
| from transformers import AutoConfig, AutoModel, AutoImageProcessor | |
| AutoConfig.register("trendyol_dinov2", TrendyolDinoV2Config) | |
| AutoModel.register(TrendyolDinoV2Config, TrendyolDinoV2Model) | |
| AutoImageProcessor.register(TrendyolDinoV2Config, TrendyolDinoV2ImageProcessor) | |
| __version__ = "1.0.0" | |
| __all__ = [ | |
| "TrendyolDinoV2Model", | |
| "TrendyolDinoV2Config", | |
| "TrendyolDinoV2ImageProcessor" | |
| ] | |