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Person, face & package — home-security detection dataset
YOLO-format dataset for person, face, vehicles, small vehicles, parcels, pets, and birds in home / delivery / street scenes. Full data lives under bigsplit/; sample/ is a 100-image preview subset (same layout: flat images/ and labels/).
Training configs point at a data.yaml beside those folders. Both splits use the same class list (nc and names); only the root path and which image set is packaged differ. Labels are YOLO .txt files with one line per box: class_id x_center y_center width height (normalized 0–1).
data.yaml — tags and layout
Class tags are the names list; order is the YOLO class index (0 … nc - 1). Below is the canonical schema used by sample/data.yaml (checked in). The full split uses the same nc and names in bigsplit/data.yaml; path, train, and val resolve to that folder’s images/ when you train from the dataset root.
sample/data.yaml
# Preview sample (100 images); same layout as bigsplit — no split subfolders.
path: .
train: images
val: images
nc: 9
names:
- person
- face
- vehicle
- bike
- bicycle
- parcel
- dog
- cat
- bird
When the full dataset is present, bigsplit/data.yaml uses the same path / train / val / nc / names (often with a file header comment describing the full split instead of the sample).
Preview (bounding boxes)
Five PNGs are generated from the checked-in sample/ images and YOLO labels. Each file stitches three frames edge-to-edge at equal height (no padding between panels). Boxes and class names are drawn from the corresponding sample/labels/*.txt files.
preview_01.png · preview_02.png · preview_03.png · preview_04.png · preview_05.png · preview_manifest.txt (source filenames per composite)
Layout
bigsplit/data.yaml— class list and paths (train/valboth point atimages/).sample/data.yaml— same schema for the preview subset.
Classes (nc: 9)
| id | name |
|---|---|
| 0 | person |
| 1 | face |
| 2 | vehicle |
| 3 | bike |
| 4 | bicycle |
| 5 | parcel |
| 6 | dog |
| 7 | cat |
| 8 | bird |
License
See LICENSE.
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