ccgdetector-fastweb-single

HanClinto/ccgdetector-fastweb-single

ccgdetector-fastweb-single is a object detection model from HanClinto. Use it when you need objects located in an image. It is set up for onnx. The card lists the license as mit.

ONNX corner detector for CollectorVision-style card scanning. This release is fastweb-single 1.39 (corndog), an EfficientViT-B0 global-token SimCC model exported for 384x384 RGB input.

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At a glance

Task: Object Detection
Library: onnx
License: mit
Access: Public
Created: Jul 13, 2026
Updated: Jul 14, 2026
SHA: 66ffd497

Model card

From the Hugging Face model README

ccgdetector-fastweb-single

ONNX corner detector for CollectorVision-style card scanning. This release is fastweb-single 1.39 (corndog), an EfficientViT-B0 global-token SimCC model exported for 384x384 RGB input.

Model details

Property	Value
Architecture	EfficientViT-B0 + global-token SimCC head
Input	384×384 RGB, ImageNet-normalized
Outputs	corners (8 floats, normalized [0,1]), compatibility presence logit, sharpness scalar
Parameters	~0.77M
File size	3.19 MB (fp32 ONNX, stripped)
Local CPU speed	8.4 ms / frame, 119.4 FPS (ONNX Runtime CPU, single thread, 384×384)
Codename	corndog
Latest version	1.39

Artifact

File	Notes
`fastweb-single-1.39.onnx`	Versioned stable-available artifact.
`model.onnx`	Alias for the same file.

Status

This release is available through the CollectorVision stable channel for the fastweb-single family. Cornelius remains the conservative stable default detector family, while fastweb-single is the smaller/faster stable-available alternative for consumers that explicitly select it.

Metrics

Normal eval, selected checkpoint epoch 39:

metric	value
validation IoU	0.953966
test IoU	0.968839
validation collapse count	77
test collapse count	13

Full ±50 rotation stress:

metric	value
mean IoU	0.974255
p10 IoU	0.960757
bad `<0.5`	1
collapse count	0

Comparison with Cornelius

Cornelius 2.12 is the stable conservative default. fastweb-single is a smaller and faster stable-available alternative with the same 384×384 input contract and the same three output tensors: corners, compatibility presence, and sharpness.

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 ONNX uses argmax SimCC decoding. Local experiments with parabolic peak refinement showed small gains, but that refinement is 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.

Outputs

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

Task: Object Detection
Library: onnx
License: mit
Created: Jul 13, 2026
Updated: Jul 14, 2026