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cornelius

HanClinto

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. 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. 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.

Object detection

Input Image→ Output Not verified

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milo

HanClinto

MobileViT-XXS backbone trained with ArcFace loss (multitask: illustration_id + set_code) to produce 128-dimensional L2-normalised embeddings of CCG card images for nearest-neighbour retrieval. The easiest way to use Milo is through the CollectorVision library, which handles corner detection, dewarping, gallery loading, and nearest-neighbour search: Gallery files built with Milo v1.0.0 use `milo1` in their filename. Embeddings from different Milo versions are **not** compatible — rebuild the gallery when upgrading.

Category pending

Input Not verified→ Output Not verified

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COST EXPLORER

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Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.