| --- |
| license: cc-by-sa-4.0 |
| task_categories: |
| - image-classification |
| tags: |
| - magic-the-gathering |
| - card-identification |
| - temporal-eval |
| - edition-identification |
| pretty_name: Sol Ring Temporal Eval |
| size_categories: |
| - n<1K |
| --- |
| |
| # Sol Ring Dataset |
|
|
| (c) 2026, HanClinto Games, LLC |
|
|
| A collection of 307 reference frames for benchmarking Magic: The Gathering card |
| identification — specifically **edition (set) discrimination** under real-world |
| camera conditions. |
|
|
| ## Purpose |
|
|
| To provide a meaningful, reproducible metric for measuring and comparing the |
| accuracy of card recognition algorithms, with particular focus on |
| **set / edition identification** rather than just card-name recognition. |
|
|
| ## Theory |
|
|
| In Magic: The Gathering, Commander is the most popular way to play the game. |
|
|
| In Commander, the single most-popular card (ranked #1 on EDHREC) is Sol Ring. |
|
|
| The Mike Bierik artwork for Sol Ring is the most-reprinted artwork in the |
| entire game, appearing across dozens of Commander precon sets with nearly |
| identical artwork and card layout. |
|
|
| This makes Sol Ring uniquely valuable as a benchmark: it is simultaneously |
| the most-played card in the most-played format, *and* the card whose printings |
| are most easily confused with one another. A system that can reliably |
| distinguish a C17 Sol Ring from a C18 Sol Ring from a CMR Sol Ring — all |
| sharing the same artwork — has demonstrated meaningful edition discrimination, |
| not just card-name lookup. |
|
|
| This dataset therefore represents a practical, high-stakes standard for edition |
| identification accuracy across a wide swath of modern sets. |
|
|
| ## Dataset construction |
|
|
| 21 distinct printings of Sol Ring were acquired through TCGPlayer — each from |
| a different edition, each bearing the iconic Mike Bierik artwork. |
|
|
| Short videos were recorded of each card using a mobile phone against a plain |
| white background, capturing dozens of frames per card across varied lightings, |
| angles, and minor motion blur. |
|
|
| Each video filename is labeled with the Scryfall UUID of the correct card. |
|
|
| Keyframes were extracted with FFmpeg, and blur detection was used to filter out |
| unwanted frames. The remaining sharp ("good") frames are what appear in this |
| dataset under `data/frames/`. |
|
|
| Corner coordinates for each frame were then detected via a SIFT homography |
| pipeline matching against the known Scryfall reference image for that card. |
| These are stored in `corners.csv` and can be used to dewarp each frame to a |
| clean, perspective-corrected card crop before running an identification model. |
|
|
| ## Temporal structure |
|
|
| Frames within each edition are **temporally ordered** by `frame_number` |
| (the source video frame index, spaced roughly every 60 source frames |
| ≈ 1–2 seconds at 30 fps). This ordering is critical for simulating a |
| live-camera rolling-buffer evaluation: |
|
|
| ```python |
| from collections import deque, defaultdict |
| import csv, cv2 |
| from pathlib import Path |
| |
| rows = list(csv.DictReader(open("corners.csv"))) |
| by_card = defaultdict(list) |
| for r in rows: |
| by_card[r["card_id"]].append(r) |
| for frames in by_card.values(): |
| frames.sort(key=lambda r: int(r["frame_number"])) |
| |
| # Simulate a rolling buffer of up to 5 embeddings |
| for card_id, frames in by_card.items(): |
| buffer = deque(maxlen=5) |
| for row in frames: |
| img = cv2.imread(row["img_path"]) |
| emb = embed(dewarp(img, row)) # your model here |
| kept = [e for e in buffer |
| if cosine_sim(emb, e) >= 0.7] # filter bad grabs |
| search_emb = normalize(mean([emb] + kept)) if kept else emb |
| top1 = gallery_search(search_emb) |
| buffer.append(emb) |
| record(top1 == card_id) |
| ``` |
|
|
| ## File layout |
|
|
| ``` |
| corners.csv 307-row metadata file (schema below) |
| data/frames/*.jpg source JPEG frames (original camera perspective, not cropped) |
| ``` |
|
|
| ## corners.csv schema |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `img_path` | str | Path relative to repo root: `data/frames/{filename}` | |
| | `card_id` | str | Scryfall UUID — ground-truth card identity | |
| | `set_code` | str | Set abbreviation parsed from filename (e.g. `khc`) | |
| | `frame_number` | int | Source video frame index — establishes temporal order within an edition | |
| | `corner0_x` … `corner3_y` | float | Homography-detected card corners, normalized 0–1 | |
| | `num_good_matches` | int | SIFT inlier count — proxy for detection confidence | |
| | `matching_area_pct` | float | Fraction of the Scryfall reference card area matched | |
|
|
| ## Edition list |
|
|
| All 21 printings share the Mike Bierik Sol Ring artwork. |
|
|
| | card_id | set | frames | frame range | |
| |---|---|---|---| |
| | `2c52c96d-e20f-4025-b759-674b36cf0db3` | AFC | 14 | 0–784 | |
| | `1b59533a-3e38-495d-873e-2f89fbd08494` | C13 | 14 | 0–780 | |
| | `b79cb394-eb91-4b3b-91d4-c6a0f723feb1` | C14 | 15 | 0–840 | |
| | `3459b229-7c46-4f70-87d4-bb31c2c17dd9` | C15 | 13 | 0–720 | |
| | `0f003fde-be17-4159-a361-711ed0bee911` | C16 | 9 | 182–662 | |
| | `c6399a22-cebf-4c1d-a23e-4c68f784ac1b` | C17 | 16 | 1–900 | |
| | `83a0f2eb-2f6d-4aaa-b7a9-ea06d5de7eca` | C18 | 18 | 0–1020 | |
| | `e672d408-997c-4a19-810a-3da8411eecf2` | C19 | 15 | 0–842 | |
| | `286bea73-8ad8-4423-8a7c-8497420fdb54` | C20 | 11 | 0–663 | |
| | `4cbc6901-6a4a-4d0a-83ea-7eefa3b35021` | C21 | 21 | 0–1200 | |
| | `199cde21-5bc3-49cd-acd4-bae3af6e5881` | CLB | 17 | 0–964 | |
| | `f9a32f17-49c4-4654-a087-1ba474f37377` | CM2 | 15 | 1–904 | |
| | `f48f7190-9ee3-477f-8b25-91e8c2916624` | CMA | 14 | 0–782 | |
| | `71357a3d-9a9f-4ec6-8e01-1966b220206c` | CMD | 13 | 0–722 | |
| | `58b26011-e103-45c4-a253-900f4e6b2eeb` | CMR | 11 | 0–720 | |
| | `beebe533-29b9-4041-ab66-0a8233c50d56` | DMC | 17 | 0–1085 | |
| | `0afa0e33-4804-4b00-b625-c2d6b61090fc` | KHC | 13 | 0–787 | |
| | `1b3a4537-1d51-47ac-a12e-6b8d68f530e6` | MB1 | 13 | 0–780 | |
| | `3917f744-b876-47ae-94ad-f72b215ff1e7` | NEC | 14 | 0–786 | |
| | `38d347b7-dc17-417a-ab07-29fe99b9a101` | PHED | 19 | 0–1143 | |
| | `8a5edac3-855a-4820-b913-44de5b29b7d0` | ZNC | 15 | 0–840 | |
| |
| ## License |
| |
| This dataset is released under the |
| [Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/). |
| |
| You are free to share and adapt this material for any purpose, including |
| commercially, as long as you provide appropriate credit and distribute any |
| derivative datasets under the same license. You are **explicitly free to use |
| this dataset for commercial purposes** under those terms. |
| |
| The goal is a universal, openly-accessible standard for measuring card |
| identification accuracy — usable for comparing closed-source and open-source |
| solutions alike. If the above terms don't work for your situation, reach out |
| and we can discuss alternative licensing. |
| |
| Contributions are welcome. Additions or corrections to the dataset are |
| appreciated but not required. |
| |
