| --- |
| license: other |
| license_name: cc-by-nc-4.0-ours-cc-by-4.0-roboflow |
| license_link: https://github.com/eschatus/diecamera/blob/main/LICENSE-NC.md |
| task_categories: |
| - image-classification |
| language: |
| - en |
| tags: |
| - dice |
| - polyhedral-dice |
| - ttrpg |
| - tabletop |
| - dice-reading |
| pretty_name: dieCamera dice crops |
| size_categories: |
| - n<10K |
| --- |
| |
| # dieCamera — per-die crops |
|
|
| One cropped image per physical die, labelled with its **type** and **face value**. This is |
| the deliberately-simple training set for dieCamera's offline value reader — the app that |
| watches a dice tray and posts the roll into a virtual tabletop |
| ([source](https://github.com/eschatus/diecamera)). |
|
|
| For the full frames these crops were cut from (and the multi-die detector-training data), see |
| the companion repo **[G-G-Games/diecamera-frames](https://huggingface.co/datasets/G-G-Games/diecamera-frames)**. |
|
|
| ## Schema |
|
|
| Standard 🤗 `imagefolder` layout — `load_dataset("G-G-Games/diecamera-crops")` needs no config. |
|
|
| ``` |
| data/<file>.jpg one die, cropped to its bounding box + a small margin |
| data/metadata.jsonl one row per crop |
| ``` |
|
|
| | column | type | meaning | |
| | ----------- | ------ | ------------------------------------------------------------------------------------------------------ | |
| | `file_name` | string | the crop image | |
| | `source` | string | `rig` (our webcam) or `roboflow:<fork>` (a third-party image, see below) | |
| | `type` | string | die type — `d4`, `d6`, `d8`, `d10`, `d12`, `d20` | |
| | `value` | int | the up-face value read (d10 may be 0) | |
| | `date` | string | capture date (`rig`), or publish date when the source has none | |
| | `added_at` | string | the day this crop first entered the dataset — filter `added_at > last_run` to train only on what's new | |
| | `holdout` | bool | `true` = reserved for evaluation; **filter these out when training** | |
|
|
| > **Training tip.** Exclude eval frames and (optionally) skip what you've already trained on: |
| > `ds.filter(lambda r: not r["holdout"])`. `added_at` lets an incremental finetune pick up only |
| > rows added since its last run, instead of reprocessing the whole set. |
| |
| Every die here has a **trusted** face value: rig dice are human-confirmed or placed to a |
| prompt; roboflow dice are the ones a human reviewed and confirmed by hand. |
| |
| ## Licence — read this before commercial use |
| |
| This dataset is **mixed-licence**, and the `source` column tells you which applies per row: |
| |
| | `source` | licence | |
| | ----------------- | -------------------------------------------------------------------------------- | |
| | `rig` | **CC BY-NC 4.0 © G-G-Games** — free personal use; commercial needs a licence | |
| | `roboflow:<fork>` | **CC BY 4.0 © the fork's original author** (commercial OK, attribution required) | |
| |
| The **labels** on every row are G-G-Games' own work (CC BY-NC 4.0). Full terms: |
| [LICENSE-NC.md](https://github.com/eschatus/diecamera/blob/main/LICENSE-NC.md). |
| |
| ### Roboflow attribution (CC BY 4.0) |
| |
| Crops with a `roboflow:` source derive from these [Roboflow Universe](https://universe.roboflow.com) |
| datasets, used with modifications (cropped; our own top-face type/value labels added): |
| |
| | `source` | original author → dataset | |
| | -------------------------- | ------------------------------------------------------------------- | |
| | `roboflow:200_dataset` | **vkr-55xr7** → https://universe.roboflow.com/vkr-55xr7/200_dataset | |
| | `roboflow:d4-turbo-rad-v4` | **turbo-rad** → https://universe.roboflow.com/turbo-rad/d4-bmzdm | |
| |
| See [ATTRIBUTIONS.md](https://github.com/eschatus/diecamera/blob/main/ATTRIBUTIONS.md) for the |
| full provenance record. |
| |
