---
license: agpl-3.0
library_name: collectorvision
tags:
- onnx
- image-retrieval
- metric-learning
- arcface
- mobilevit
base_model: apple/mobilevit-xx-small
---

# Milo — CCG Card Embedder

MobileViT-XXS backbone trained with ArcFace loss (multitask: illustration_id + set_code) to produce 128-dimensional L2-normalised embeddings of CCG card images for nearest-neighbour retrieval.

## Model details

| Property | Value |
|---|---|
| Architecture | MobileViT-XXS + linear projection |
| Input | 448×448 RGB, ImageNet-normalised |
| Output | 128-d L2-normalised embedding vector |
| Parameters | ~1.0M |
| File size | 5.2 MB (fp32 ONNX) |
| Codename | milo |
| Version | 1.0.0 (epoch 15) |
| Training labels | illustration_id + set_code (multitask ArcFace) |

## Usage

The easiest way to use Milo is through the [CollectorVision](https://github.com/HanClinto/CollectorVision) library, which handles corner detection, dewarping, gallery loading, and nearest-neighbour search:

```python
import collector_vision as cvg

cvid = cvg.Identifier(cvg.HFD("HanClinto/milo", "scryfall-mtg"))
result = cvid.identify("photo.jpg")
print(result.ids)         # {"scryfall_id": "..."}
print(result.confidence)  # 0.94
```

### Direct ONNX usage

```python
import onnxruntime as ort
import numpy as np
from PIL import Image

session = ort.InferenceSession("model.onnx")

# Preprocess: resize to 448×448, ImageNet normalise, NCHW float32
img = Image.open("card_crop.jpg").convert("RGB").resize((448, 448))
x = np.array(img, dtype=np.float32) / 255.0
x = (x - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
x = x.transpose(2, 0, 1)[None]  # (1, 3, 448, 448)

emb = session.run(None, {"pixel_values": x})[0]  # (1, 128) float32, L2-normalised
```

Cosine similarity between two embeddings is just their dot product (both are unit vectors).

## Gallery compatibility

Gallery files built with Milo v1.0.0 use `milo1` in their filename. Embeddings from different Milo versions are **not** compatible — rebuild the gallery when upgrading.

## Part of CollectorVision

Used together with [HanClinto/cornelius](https://huggingface.co/HanClinto/cornelius) in the [CollectorVision](https://github.com/HanClinto/CollectorVision) inference library.