Cornelius — CCG Card Corner Detector

MobileViT-XXS corner detector 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 + global-token SimCC head
Input	384×384 RGB, ImageNet-normalised
Outputs	corners (8 floats, normalised [0,1]), compatibility presence logit, sharpness scalar
Parameters	~1.06M
File size	4.2 MB (fp32 ONNX, simplified)
Local CPU speed	23.2 ms / frame, 43.0 FPS (ONNX Runtime CPU, single thread, 384×384)
Codename	cornelius
Latest version	2.12

Versions

File	Notes
cornelius-2.12.onnx / model.onnx	Global-token SimCC production model. Smaller than 1.x (~1.06M params vs ~1.82M) and substantially more robust under rotation/occlusion. Full-rotation stress: mean IoU 0.970411, p10 0.954621, 0 bad, 0 collapse.
cornelius-1.221.onnx	Fixed SimCC coordinate decoding to use peak argmax instead of soft expectation. This addresses rotated-card failures where multi-modal corner distributions decoded to implausible between-peak coordinates; see CollectorVision issue #24.
cornelius-1.210.onnx	Earlier beta-test candidate. Combined held-out IoU 0.8857.

Evaluation

All IoU values are quadrilateral IoU between predicted and labelled card corners. bad counts predictions with IoU < 0.5; collapse counts geometrically degenerate predictions where two predicted corners nearly coincide.

Release comparison

Version	Architecture	Params	ONNX size	Normal held-out IoU	Full ±50° rotation mean / p10	Full-rotation bad / collapse	Notes
2.12	MobileViT-XXS + global-token SimCC	~1.06M	4.2 MB	0.968911	0.970411 / 0.954621	0 / 0	Current default model.onnx; selected production checkpoint.
1.221	MobileViT-XXS + independent SimCC	~1.82M	7.1 MB	—	0.933910 / 0.928732	138 / 91	Strong normal detector, but vulnerable to rotation-induced corner collapse.
1.210	MobileViT-XXS + independent SimCC	~1.82M	7.1 MB	0.8857 combined	—	—	Earlier beta; reported combined held-out IoU 0.8857.

Cornelius vs fastweb-single

Cornelius remains the stable, conservative default. The fastweb-single family is also available through the stable channel for consumers that explicitly select it; it is a smaller EfficientViT-B0 global-token SimCC detector being evaluated as a faster replacement candidate. Both models use the same 384×384 input contract and produce corners, compatibility presence, and sharpness outputs.

Model	Channel	Normal test IoU	Normal collapse	Full ±50° mean / p10 IoU	Full-rotation bad / collapse	ONNX size	Params	Local CPU speed
Cornelius 2.12	stable	0.968911	18	0.970411 / 0.954621	0 / 0	4.41 MB	1.05M	23.2 ms / 43.0 FPS
fastweb-single 1.39	stable + testing	0.968839	13	0.974255 / 0.960757	1 / 0	3.19 MB	0.77M	8.4 ms / 119.4 FPS

Takeaways:

• Normal accuracy is effectively tied: fastweb-single trails Cornelius by 0.000072 IoU on the normal held-out test.
• fastweb-single is about 28% smaller on disk and about 2.8× faster in the local single-thread CPU benchmark.
• fastweb-single has stronger rotation-stress mean and p10 IoU, but still has one remaining bad rotation-stress case where Cornelius has zero.
• The current published fastweb-single ONNX uses the same argmax SimCC decoding style; small post-hoc parabolic peak-refinement gains were observed in local experiments but are not baked into this artifact.

Speed benchmark details: ONNX Runtime 1.24.4, CPUExecutionProvider, intra_op_num_threads=1, inter_op_num_threads=1, 50 warmup runs and 300 timed runs on a 384×384 float32 input. These numbers are intended for model-to-model comparison on the same machine, not as universal device benchmarks.

Cornelius 2.12 checkpoint selection

The 2.12 release was selected from a low-LR continuation because it best balanced normal accuracy and stress robustness:

Checkpoint	Normal test IoU	Normal collapse	Full ±50° mean IoU	p10 IoU	bad	collapse
epoch 9	0.968923	19	0.968892	0.952787	1	0
epoch 12 / 2.12	0.968911	18	0.970411	0.954621	0	0
epoch 14	0.968777	18	0.968660	0.952481	0	0

Occlusion + rotation smoke test

Three hand-picked occlusion stress samples were swept through the same full rotation preset. The 2.12 numbers below are from the Hugging Face-downloaded model.onnx.

Model	Mean IoU	p10 IoU	bad	collapse
Cornelius 2.12	0.888789	0.795696	0	0
Frozen global-token prototype	0.772039	0.673141	0	0
Hydra4 reduce4_h64 prototype	0.736071	0.648447	0	0
Cornelius 1.x baseline	0.687937	0.406498	22	21

Per-sample Cornelius 2.12 results:

Sample	Mean IoU	p10 IoU	min IoU	bad	collapse
Ingenious Skaab	0.963076	0.951756	0.939898	0	0
LTR 566	0.883024	0.847024	0.836244	0	0
Wall of Fire	0.820269	0.779252	0.763541	0	0

Outputs

• corners — 8 floats [x0,y0, x1,y1, x2,y2, x3,y3] in TL→TR→BR→BL order, normalised [0,1]
• presence — compatibility logit. For 2.x this is a constant 1.0; prefer the sharpness gate.
• sharpness — mean peak of the 8 SimCC softmax distributions; use this for card-present gating.

Usage

The easiest way to use Cornelius is through the CollectorVision library, which wires it into a full detect → dewarp → embed → identify pipeline:

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

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, {"image": x})
# corners: (1, 8) — x0,y0,x1,y1,x2,y2,x3,y3 normalised [0,1], TL→TR→BR→BL
# presence: (1,) — compatibility logit; use sharpness instead for gating
# sharpness: (1,) — mean peak of the 8 SimCC softmax distributions

if sharpness[0] > 0.02:
    pts = corners[0].reshape(4, 2)

Part of CollectorVision

Used together with HanClinto/milo in the CollectorVision inference library.