Favnavi-Vision-Ecommerce-V1-Base
Favnavi-Vision-Ecommerce-V1 is a visual feature model specialized for product recognition and retrieval in the e-commerce domain.
Usage
FavnaviImageEncoderV1 is a custom implementation, so trust_remote_code=True is required only when loading the model.
AutoImageProcessor can be loaded without setting trust_remote_code=True.
import torch
from transformers import AutoModel, AutoImageProcessor
from transformers.image_utils import load_image
# Load the model and processor
model_name = "ly-corporation/favnavi-vision-ecommerce-v1-base"
model = AutoModel.from_pretrained(model_name, trust_remote_code=True).eval()
processor = AutoImageProcessor.from_pretrained(model_name)
# Load the image
image = load_image("https://huggingface.co/datasets/merve/coco/resolve/main/val2017/000000000285.jpg")
inputs = processor(images=[image], return_tensors="pt").to(model.device)
# Run inference
with torch.no_grad():
image_embeddings = model.get_image_features(**inputs)
print(image_embeddings.shape)
from transformers import pipeline
from transformers.image_utils import load_image
pipe = pipeline(
task="image-feature-extraction",
model="ly-corporation/favnavi-vision-ecommerce-v1-base",
trust_remote_code=True,
)
image = load_image("https://huggingface.co/datasets/merve/coco/resolve/main/val2017/000000000285.jpg")
embedding = pipe(image, return_tensors=True)
print(embedding.shape)
Models
| Name | Pretrained model | Dim | In-house eval (mAP@20) | ILIAS small (mAP@1k) | ILIAS full (mAP@1k) |
|---|---|---|---|---|---|
| ly-corporation/favnavi-vision-ecommerce-v1-base | google/siglip2-base-patch16-naflex | 256 | 0.861 | 31.9 | 27.7 |
| ly-corporation/favnavi-vision-ecommerce-v1-large | google/siglip2-so400m-patch16-naflex | 256 | 0.894 | 44.7 | 40.6 |
- ILIAS small/full results are evaluated on the ILIAS dataset.
- Training data: A subset of an in-house e-commerce dataset
- Training objective: Trained with ArcFace over 535K product classes (the ArcFace class embeddings are not included in the model weights)
License
Citation
@misc{favnavi-vision-ecommerce-v1,
title = {Favnavi Vision E-commerce V1: A Visual Feature Model for Product Retrieval in the E-commerce Domain},
author = {Nishimura, Shuhei and Doi, Kenji and Yamashita, Fumiya and Yonebayashi, Dai and Iwasaki, Masajiro},
year = {2026},
howpublished = {\url{https://huggingface.co/ly-corporation/favnavi-vision-ecommerce-v1-base}}
}