Datasets:
File size: 4,718 Bytes
581eeda a3ff0d7 581eeda 8666aa2 581eeda 8666aa2 581eeda 8666aa2 581eeda 8666aa2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | ---
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.
|