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
File size: 782 Bytes
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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"
]
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