---
license: apache-2.0
base_model:
- facebook/dinov3-vitl16-pretrain-lvd1689m
pipeline_tag: image-feature-extraction
tags:
- product_recognition
- image_embedding
---

# Cat2Real-DINOv3-384
Cat2Real-DINOv3-384 is a product image embedding model fine-tuned from DINOv3. It maps the real-world product images and catalog packshots to the same embedding space for similarity search.

# How to Get Started with the Model
The example below demonstrates how to obtain an image embedding with the [AutoModel] class.

```python
import torch
import cv2
from torchvision import transforms
from transformers import AutoImageProcessor, AutoModel
from transformers.image_utils import load_image

image_path = "your local image path"
dim = 384
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (dim, dim))
transform = transforms.ToTensor()
image = transform(image)
image = image.unsqueeze(0)
model_name = "zhanganyi88/Cat2Real-DINOv3-384"
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModel.from_pretrained(
    model_name, 
    device_map="auto", 
)
inputs = processor(images=image, return_tensors="pt").to(model.device)
with torch.inference_mode():
    outputs = model(**inputs)
embedding = outputs.pooler_output
print("embedding shape:", embedding.shape)
```