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license: agpl-3.0
library_name: collectorvision
tags:
- onnx
- object-detection
- card-detection
- mobilevit
base_model: apple/mobilevit-xx-small
---
# Cornelius — CCG Card Corner Detector
MobileViT-XXS backbone with a SimCC coordinate classification head, trained to locate the four corners of a CCG card (Magic: The Gathering, Pokémon, etc.) in a photograph or video frame.
## Model details
| Property | Value |
|---|---|
| Architecture | MobileViT-XXS + SimCC head |
| Input | 384×384 RGB, ImageNet-normalised |
| Outputs | corners (8 floats, normalised [0,1]), presence logit, sharpness scalar |
| Parameters | ~1.82M |
| File size | 8.6 MB (fp32 ONNX) |
| Codename | cornelius |
| Version | 1.0.0 (epoch 45) |
## Outputs
- **corners** — 8 floats `[x0,y0, x1,y1, x2,y2, x3,y3]` in TL→TR→BR→BL order, normalised [0,1]
- **presence** — raw logit; unreliable on blank images, prefer the sharpness gate
- **sharpness** — mean peak of the 8 SimCC softmax distributions; blank frames ≈ 0.008, valid cards ≈ 0.03–0.07
## Usage
The easiest way to use Cornelius is through the [CollectorVision](https://github.com/HanClinto/CollectorVision) library, which wires it into a full detect → dewarp → embed → identify pipeline:
```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": "..."}
```
### Direct ONNX usage
```python
import onnxruntime as ort
import numpy as np
from PIL import Image
session = ort.InferenceSession("model.onnx")
# Preprocess: resize to 384×384, ImageNet normalise, NCHW float32
img = Image.open("photo.jpg").convert("RGB").resize((384, 384))
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, 384, 384)
corners, presence, sharpness = session.run(None, {"pixel_values": x})
# corners: (1, 8) — x0,y0,x1,y1,x2,y2,x3,y3 normalised [0,1], TL→TR→BR→BL
# presence: (1, 1) — raw logit
# sharpness: (1, 1) — use > 0.02 as a card-present gate
if sharpness[0, 0] > 0.02:
pts = corners[0].reshape(4, 2) # (4, 2) normalised corners
```
## Part of CollectorVision
Used together with [HanClinto/milo](https://huggingface.co/HanClinto/milo) in the [CollectorVision](https://github.com/HanClinto/CollectorVision) inference library.
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