---
license: other
task_categories:
  - object-detection
  - image-classification
tags:
  - waste
  - recycling
  - smart-bin-recognition
# THE VIEWER, and what it can honestly show.
#
# Without this block the viewer fails with "The dataset generation failed": the
# layout is a YOLO detection tree - JPEGs beside .txt labels, two of the three
# subsets sharded two levels deep - and nothing in it is a format the viewer
# auto-detects.
#
# These configs expose the IMAGES so the corpus can be browsed. They deliberately
# do NOT claim to expose the labels: a YOLO .txt beside each frame is not a
# feature the viewer can render, and a config that silently dropped them while
# looking complete would be worse than one that says so. Use snapshot_download
# for training data; the viewer is for looking.
#
# `drop_labels: true` matters on the sharded subsets. imagefolder infers a class
# label from the directory name, and those directories are `00`..`ff` - the first
# two characters of sha256(stem), chosen because the Hub rejects a push where any
# directory holds more than 10 000 files. Without this the viewer would show a
# 256-class label column that means nothing at all.
configs:
  - config_name: legacy
    drop_labels: true
    data_files:
      - split: train
        path: legacy/images/*.jpg
  - config_name: open_images
    drop_labels: true
    data_files:
      - split: train
        path: open_images/images/*/*.jpg
  - config_name: negatives
    drop_labels: true
    data_files:
      - split: train
        path: negatives/images/*/*.jpg
---

# arudaev/smart-bin-detect

Training data for **Smart Bin Recognition** – a validator ("is there a bin?")
and an identifier ("which bin?"). The design lives in `docs/04-ml-pipeline.md`
in the project repo, which is private; the manifests here carry per-image
provenance and are the authoritative record of what this dataset contains.

**Every image carries provenance**: source, source URL, licence, region,
capture date, annotator where known, label origin (`human` / `machine` /
`legacy` / `open-images`) and adjudication status. Licences differ per subset and
are recorded per image; there is no single licence for the whole repo, which is
why the header says `other`.

## Subsets

### `legacy`

| | |
|---|---|
| frames | 370 |
| boxes | 403 |
| crops | 403 |
| crops pending adjudication | 403 |
| bins per frame | {1: 341, 2: 25, 3: 4} |
| licence | MIT |
| region | de-by-deggendorf |
| label origin | legacy |

### `open_images`

| | |
|---|---|
| frames | 1110 |
| boxes | 1936 |
| crops | 0 |
| crops pending adjudication | 0 |
| bins per frame | {1: 708, 2: 206, 3: 98, 4: 40, 5: 28, 6: 13, 7: 8, 8: 5, 9: 2, 12: 1, 18: 1} |
| licence | https://creativecommons.org/licenses/by/2.0/ |
| region | unknown |
| label origin | open-images |

### `negatives`

| | |
|---|---|
| frames | 17474 |
| boxes | 0 |
| crops | 0 |
| crops pending adjudication | 0 |
| bins per frame | {0: 17474} |
| licence | https://creativecommons.org/licenses/by/2.0/ |
| region | unknown |
| label origin | open-images |


## How to read this

Each subset directory is a self-contained pool:

```
<subset>/
├── manifest.json   provenance for every frame, and every crop
├── images/         full frames
├── labels/         YOLO labels; an EMPTY file means a deliberate background image
└── crops/          identifier candidates (present only where relevant)
```

Large subsets interpose a **shard** level – `images/ab/<id>.jpg`, the two hex
characters being `sha256(stem)` – because the Hub rejects a push where any
directory holds more than 10 000 files, and the negative corpus alone is
~17 500. `manifest.json` says which: `"layout": "sharded"`, and a manifest
without the key is flat. `legacy/` is flat and stays that way.

`sbr.dataset.prepare.build_yolo_tree` assembles them into a training tree,
grouping frames by capture cluster so that two photographs of the same bin can
never straddle a split.

## What this data is not

The legacy subset is **one city in one week**, 370 usable frames, and not one
frame of it holds four or more bins — every multi-bin frame here comes from
Open Images. Numbers measured on `legacy` alone are in-distribution numbers.

**There is no geographic holdout.** Every Open Images frame carries
`region_id: "unknown"`, because the source does not record where a photograph
was taken, so no split in this dataset answers "does it work in another city".
See `docs/11-phase2-results.md` in the project repo for what was measured, on
which split, and on what hardware.