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End of preview. Expand in Data Studio
Rock Paper Scissors (Roboflow, LibreYOLO format)
Hand gestures for rock, paper and scissors, with bounding boxes, ready to train with LibreYOLO. It is the full labelled set behind the LibreYOLO hands-on at Mondragon Unibertsitatea on 7 October 2026. The photos the room labels that day live in Xuban/rock-paper-scissors-room.
Source and licence
The photos and boxes come from the Roboflow Universe project Rock Paper Scissors by rock-paper-scissors-2, version 3 (2,174 images), published under CC BY 4.0. This copy keeps that licence. If you use it, credit the original project:
rock-paper-scissors-2, Rock Paper Scissors dataset, Roboflow Universe, https://universe.roboflow.com/rock-paper-scissors-2/rock-paper-scissors-bwev7, CC BY 4.0.
Changes from the original: class names and ids were remapped, some photos were left out (see below), the photos were renamed, and the split is new.
Classes
| id | name | Roboflow name |
|---|---|---|
| 0 | rock | Rock |
| 1 | paper | Paper |
| 2 | scissors | Scissor |
Layout
data.yaml
images/train/*.jpg labels/train/*.txt
images/val/*.jpg labels/val/*.txt
data.yaml has train: images/train, val: images/val and the three names, with no path: line, so it works wherever the folder is downloaded. Labels are YOLO text files, one box per line: class id, then centre x, centre y, width and height, all relative to the image size.
from huggingface_hub import snapshot_download
from libreyolo import LibreYOLO
data = snapshot_download("Xuban/rock-paper-scissors-roboflow", repo_type="dataset")
model = LibreYOLO("LibreYOLO9t.pt")
model.train(data=f"{data}/data.yaml", epochs=25)
Counts
| split | photos | rock boxes | paper boxes | scissors boxes |
|---|---|---|---|---|
| train | 1,485 | 755 | 809 | 409 |
| val | 100 | 58 | 56 | 46 |
What was left out, and why
- 351 photos on a green background. Their boxes are shifted off the hands, so they would teach the model the wrong place.
- 22 3D-rendered hands (rock1.png and similar). They are not photos, and the hands-on is about real cameras.
- 216 photos that come from the same sources as the validation photos. The split is by source photo (and by blocks of 15 frames for the two webcam series), so near-identical frames never appear in both train and val. Those 216 were not added to val, so the validation set stays the same fixed 100 photos used in the room's dataset and scores can be compared.
The validation set is fixed: the same 100 photos, with Roboflow's labels, are the validation set of the room's dataset. File names: rf_, rfx_ and rfr_ are training photos, val_ are validation photos.
The folder is built by make_rps_data.py --full in the course repository, with a fixed seed.
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