Instructions to use HanClinto/cornelius with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- CollectorVision
How to use HanClinto/cornelius with CollectorVision:
pip install git+https://github.com/HanClinto/CollectorVision huggingface_hub
from huggingface_hub import hf_hub_download import collector_vision as cvg checkpoint = hf_hub_download(repo_id="HanClinto/cornelius", filename="model.onnx") # Detector models, such as Cornelius: detector = cvg.NeuralCornerDetector(checkpoint) # Embedder models, such as Milo: embedder = cvg.NeuralEmbedder(checkpoint)
- Notebooks
- Google Colab
- Kaggle
Align Cornelius comparison with fastweb-single
Browse files
README.md
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@@ -23,6 +23,7 @@ MobileViT-XXS corner detector trained to locate the four corners of a CCG card
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| Outputs | corners (8 floats, normalised [0,1]), compatibility presence logit, sharpness scalar |
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| Parameters | ~1.06M |
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| File size | 4.2 MB (fp32 ONNX, simplified) |
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| Codename | cornelius |
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| Latest version | 2.12 |
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| 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. |
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| 1.210 | MobileViT-XXS + independent SimCC | ~1.82M | 7.1 MB | 0.8857 combined | — | — | Earlier beta; reported combined held-out IoU 0.8857. |
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### Cornelius 2.12 checkpoint selection
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The 2.12 release was selected from a low-LR continuation because it best balanced
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| Outputs | corners (8 floats, normalised [0,1]), compatibility presence logit, sharpness scalar |
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| Parameters | ~1.06M |
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| File size | 4.2 MB (fp32 ONNX, simplified) |
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| Local CPU speed | 23.2 ms / frame, 43.0 FPS (ONNX Runtime CPU, single thread, 384×384) |
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| Codename | cornelius |
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| Latest version | 2.12 |
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| 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. |
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| 1.210 | MobileViT-XXS + independent SimCC | ~1.82M | 7.1 MB | 0.8857 combined | — | — | Earlier beta; reported combined held-out IoU 0.8857. |
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### Cornelius vs fastweb-single
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Cornelius remains the stable, conservative default. The `fastweb-single` testing
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model is a smaller EfficientViT-B0 global-token SimCC detector that is being
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evaluated as a faster replacement candidate. Both models use the same 384×384
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input contract and produce `corners`, compatibility `presence`, and `sharpness`
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outputs.
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| Model | Channel | Normal test IoU | Normal collapse | Full ±50° mean / p10 IoU | Full-rotation bad / collapse | ONNX size | Params | Local CPU speed |
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|---|---|---:|---:|---:|---:|---:|---:|---:|
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| Cornelius 2.12 | stable | **0.968911** | 18 | 0.970411 / 0.954621 | **0 / 0** | 4.41 MB | 1.05M | 23.2 ms / 43.0 FPS |
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| fastweb-single 1.39 | testing | 0.968839 | **13** | **0.974255 / 0.960757** | 1 / 0 | **3.19 MB** | **0.77M** | **8.4 ms / 119.4 FPS** |
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Takeaways:
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- Normal accuracy is effectively tied: fastweb-single trails Cornelius by
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0.000072 IoU on the normal held-out test.
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- fastweb-single is about 28% smaller on disk and about 2.8× faster in the
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local single-thread CPU benchmark.
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- fastweb-single has stronger rotation-stress mean and p10 IoU, but still has
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one remaining bad rotation-stress case where Cornelius has zero.
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- The current published fastweb-single ONNX uses the same argmax SimCC decoding
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style; small post-hoc parabolic peak-refinement gains were observed in local
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experiments but are not baked into this artifact.
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Speed benchmark details: ONNX Runtime 1.24.4, CPUExecutionProvider,
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`intra_op_num_threads=1`, `inter_op_num_threads=1`, 50 warmup runs and 300 timed
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runs on a 384×384 float32 input. These numbers are intended for model-to-model
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comparison on the same machine, not as universal device benchmarks.
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### Cornelius 2.12 checkpoint selection
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The 2.12 release was selected from a low-LR continuation because it best balanced
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