Datasets:
File size: 4,207 Bytes
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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.
|