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foggy9/trendyol-dino-v2.1-ecommerce-256d

sourceHugging Facecc-by-sa-4.0updated 19d agoView on Hugging Face

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Trendyol DinoV2.1 Image Similarity Model

Fine-tuned DinoV2 (ViT-B/14) with [GeM pooling](https://arxiv.org/abs/1711.02512) for e-commerce product image retrieval. This is the successor to `Trendyol/trendyol-dino-v2-ecommerce-256d`.

Paper: TBD — "Visual Search at Trendyol" (in review)

Model Details

—Model Type: Image Similarity / Retrieval
—Architecture: DinoV2 ViT-B/14 + GeM pooling + linear projection (ArcFace-trained)
—Embedding Dimension: 256
—Input Size: 224×224
—Checkpoint: ray-dinov2-full_catalog_1000_20-pfc-gem-mlp-run_12 epoch 09
—Framework: PyTorch / SafeTensors

What's new vs v2

	v2	v2.1
Pooling / head	Flatten spatial tokens → Linear(196608→256)	[GeM](https://arxiv.org/abs/1711.02512) → Linear(768→256)
Training data	300 distinct products per ~3400 categories	1000 distinct products per ~3400 categories
Preprocess	Lanczos/JPEG/332 pad pipeline	Scale+pad to 224 (training inference preprocess)

Quick Start

python

import torch
from PIL import Image
from transformers import AutoModel, AutoImageProcessor

device = "cuda" if torch.cuda.is_available() else "cpu"
repo = "Trendyol/trendyol-dino-v2.1-ecommerce-256d"

processor = AutoImageProcessor.from_pretrained(repo, trust_remote_code=True)
model = AutoModel.from_pretrained(repo, trust_remote_code=True).to(device).eval()

image = Image.open("your_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}

with torch.no_grad():
    embeddings = model(**inputs).last_hidden_state  # [1, 256]

print(embeddings.shape)

Preprocessing Pipeline

1.ScaleImage: resize so max side = 224 (keep aspect ratio)
2.PadToSquare: pad with color 255
3.Resize: 224×224
4.ToTensor + ImageNet Normalize (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

Installation

bash

pip install transformers torch torchvision safetensors pillow numpy

Intended Use

—Product image similarity search
—Visual recommendations / duplicate detection
—Content-based retrieval in e-commerce

Limitations

—Optimized for product / e-commerce images
—Requires trust_remote_code=True
—Classification heads from training are not included in this Hub export (embeddings only)

License

See LICENSE. Same terms as the v2 release: source-available; commercial use requires attribution and prior notification to Trendyol (scr.datascience@trendyol.com).

Citation

@misc{trendyol-dinov2-ecommerce-v21,
  title={Trendyol DinoV2.1 E-commerce Image Similarity Model},
  author={Trendyol Data Science Team},
  year={2026},
  url={https://huggingface.co/Trendyol/trendyol-dino-v2.1-ecommerce-256d}
}

@article{radenovic2018gem,
  title={Fine-tuning CNN Image Retrieval with No Human Annotation},
  author={Radenovi{\'c}, Filip and Tolias, Giorgos and Chum, Ond{\v{r}}ej},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={41},
  number={7},
  pages={1655--1668},
  year={2018},
  doi={10.1109/TPAMI.2018.2846566},
  note={GeM pooling; also arXiv:1711.02512}
}

@misc{trendyol-visual-search-tbd,
  title={Visual Search at Trendyol},
  author={Trendyol Data Science Team},
  year={2026},
  note={Paper TBD (in review)}
}