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

Versions

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

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.

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:

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.

Per-sample Cornelius 2.12 results:

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.