---
license: agpl-3.0
library_name: onnx
pipeline_tag: object-detection
tags:
  - waste
  - recycling
  - smart-bin-recognition
  - yolo11
  - onnx
  - int8
base_model:
  - Ultralytics/YOLO11
datasets:
  - arudaev/smart-bin-detect
---

# Smart Bin Recognition — validator and identifier

Two ONNX graphs behind a web app that answers *"what is this bin, and what goes
in it?"* from a phone camera. They run **server-side**; nothing here is
downloaded to a device.

**One of them may ship. The other may not, and the service refuses to start on
it.** Both are published because an artefact that failed its gate is evidence,
not waste, and hiding it would make the next person repeat the measurement.

| | **validator** | **identifier** |
|---|---|---|
| question | *is there a bin?* | *which bin?* |
| file | `v1/validator-v1.onnx` | `v1/identifier-v1.onnx` |
| architecture | YOLO11n detection | YOLO11s-cls |
| input | 448 × 448, NCHW, static batch 1 | 320 × 320, NCHW, **dynamic batch** |
| weights | int8 (U8S8, per-channel) | int8 (U8S8, per-channel) |
| size | 3.15 MB | 5.81 MB |
| **`may_ship`** | **`false`** | **`true`** |

**Every number below carries the split it was measured on and the hardware it
was measured on.** A figure without both is not quotable, here or in the project
that produced it.

---

## The class list, and its order

**Read the sidecar, not this table.** `classes` in each `*.json` is the
authoritative order and the service reads it from there. The order **is** the
ONNX output index; reordering it silently invalidates every deployed model, and
the project pins it with a test.

**validator** — 1 class:

| index | id |
|---:|---|
| 0 | `bin` |

**identifier** — 3 classes, **alphabetical**, which is the order the training
framework emitted and therefore the order the head produces. It is *not* the
order the taxonomy lists them in:

| index | id | what it is |
|---:|---|---|
| 0 | `igloo` | a glass/bottle bank |
| 1 | `wheelie_large` | a 660–1100 L four-wheeled container |
| 2 | `wheelie_small` | a 120–240 L two-wheeled household bin |

These are **form factors — shapes, not meanings**. The model never predicts a
waste stream. Meaning comes from a per-jurisdiction region pack (a JSON file)
that maps *(form factor, colour, region)* to a stream, so adding a country is a
data change rather than a retrain.

---

## The identifier — passes every gate

| gate | budget | measured | split / hardware |
|---|---:|---:|---|
| int8 accuracy cost | ≤ 0.02 top-1 | **0.0000** | `test`, Kaggle T4 training run |
| median latency | ≤ 25 ms / crop | **9.921 ms** | GCE `n2-standard-4`, 2 of 4 vCPU pinned, Intel Cascade Lake, `europe-west3-a`, onnxruntime 1.29.0, `representative: true` |
| p95 latency | – | 11.409 ms | same |
| hardware named | required | yes | same |

`gate_result: {"failures": [], "unmeasured": [], "may_ship": true}`.

### Its evidence is thin, and the card says so

**`test` top-1 is 1.0000 on 47 crops.**

| class | crops in `test` | capture clusters in `test` |
|---|---:|---:|
| `wheelie_small` | 25 | 9 |
| `wheelie_large` | 19 | 8 |
| **`igloo`** | **3** | **2** |

- 47 correct out of 47 is **not** certainty. By the rule of three the 95 % lower
  bound is **0.936**.
- **`igloo`'s contribution is three crops from two scenes.** Its per-class number
  is noisy and must never be quoted clean.
- **The better estimate is 0.9834** — out-of-fold accuracy over all 403 crops
  under `GroupKFold` on capture cluster, against a 0.6823 majority-class
  baseline. A random split of these crops measures memorisation: 403 crops come
  from 100 capture clusters, and the largest holds 18 photographs of one bin.

**The task is close to saturated**, so this measurement can distinguish *"int8
and fp32 differ by at most one crop"* from *"they differ by two or more"* and not
much finer. The gate passed honestly; it did not pass by a margin the data can
resolve.

---

## The validator — trained, real, and it cannot ship

It is not a failed training run. It is a working detector that **int8
quantisation destroys**.

| gate | budget | measured | split / hardware |
|---|---:|---:|---|
| **int8 accuracy cost** | ≤ 0.02 mAP@0.5 | **0.727** ❌ | `test` |
| median latency | ≤ 50 ms | 18.252 ms ✅ | GCE `n2-standard-4`, Cascade Lake, 2 vCPU pinned, `representative: true` |
| p95 latency | – | 21.345 ms | same |

| | value | split |
|---|---:|---|
| mAP@0.5, PyTorch fp32 reference | **0.7524** | `test` |
| mAP@0.5, the int8 graph published here | **0.025** | `test` |
| specificity on background frames | 0.9793 | 2 662 hard negatives |

`gate_result: {"failures": ["int8 quantisation cost 0.727 map50 (max 0.02)"], "may_ship": false}`.

**The service loads the validator unconditionally and refuses to start on an
artefact whose sidecar says `may_ship: false`. So nothing is deployed, and that
is correct rather than pending.**

### What is known about why

- **Quantising the detection head is what collapses it.** Leaving `/model.23/`
  in fp32 takes the graph from 0.015 to **0.7481** on `val` — a fifty-fold
  recovery — for about +5.7 ms and +1.2 MB on an x86 proxy.
- **It does not follow that nothing else matters.** That graph is still quantised
  everywhere else and still loses **0.0252** against a 0.02 budget. It misses by
  **0.0052**, and the residual is unattributed.
- The three remedies onnxruntime names for this failure mode — S8S8,
  `reduce_range`, U8U8 — all stay at collapse, as does per-tensor. This is *not*
  the x86 saturation case it resembles.
- **Post-training int8 over the whole graph is not viable for this
  architecture.** Any future YOLO11 detection export here starts from
  `exclude_head=True`.

### fp32 is the open route, and it was measured

Measured 2026-08-22, both formats on **one** Cascade Lake instance with the arms
alternated:

| | int8 | fp32 |
|---|---:|---:|
| validator p50 | 17.921 ms | **24.605 ms** |
| against a 50 ms budget | passes | **passes** |
| concurrent scanners @ 1 bin | 5 | **4** |
| accuracy cost | −0.727 mAP | 0.0 |

**An fp32 validator meets the latency budget with 25 ms to spare and costs one
concurrent scanner.** The ship gate currently refuses unquantised artefacts
outright, and its stated reason — *"it will not meet the latency budget"* — is
false on this architecture at this input size. Splitting that gate into
per-format profiles is staged for the maintainer's decision and is **not**
merged. **No fp32 artefact is published in this repository.**

---

## Serving contract

Both graphs, from the sidecars:

- **Normalisation:** scale `1/255`, mean `[0,0,0]`, std `[1,1,1]`. No ImageNet
  statistics.
- **Input name:** `images`. Layout `NCHW`.
- **NMS is not in the graph.** Apply it yourself: `iou 0.45`, `score 0.35`.
- **The identifier accepts a batch > 1 and the validator does not.** A frame can
  hold several bins — a bank of six is a normal input, not an edge case — so the
  crops go through one call. The validator sees exactly one frame, every time.

Read `imgsz`, `classes`, `normalisation` and `nms` **from the sidecar** rather
than from this card. The service does, which is why a model swap needs no code
change.

---

## Coverage gap — six of ten form factors have no data at all

The taxonomy defines ten form factors. The identifier was trained on **three**.

| form factor | training crops | status |
|---|---:|---|
| `wheelie_small` | 247 | trained |
| `wheelie_large` | 115 | trained |
| `igloo` | 40 | trained |
| `street_basket` | **1** | **dropped** — one crop in one capture cluster cannot be split across train/val/test, so it can be neither trained nor evaluated |
| `underground`, `textile_bank`, `sack`, `crate`, `wall_unit`, `container_bank` | **0** | **no data** |

**Those six keep their ids.** An id with no training data is a coverage gap, not
a deletion. Everything the identifier has never seen resolves to `unknown`, which
is a designed state with a real interface, and the honest answer.

**Open Images cannot close this gap.** A frozen survey of 384 boxes found it to
be a street-litter corpus — 35 % `street_basket`, and **zero** `underground`,
`textile_bank` or `wall_unit`.

## There is no geographic holdout

Every training frame comes from **one city in one week** (Deggendorf, Bavaria),
plus Open Images frames whose `region_id` is `unknown` because the source does
not record where a photograph was taken. **No split in this data answers "does it
work in another city."** The targets `min_recall_heldout_city` and
`min_formfactor_acc_heldout_city` are reported as **unmeasurable**, not as
passed.

Do not quote the predecessor project's 95.2 % mAP as a baseline for these models.
It was measured on a random split of one week's photographs in one city.

---

## What this must not be used for

- **Do not use the validator as published.** It scores 0.025 mAP@0.5. It is here
  as the evidence behind a `may_ship: false`, not as a detector.
- **Do not derive disposal advice from these models alone.** They output a shape.
  Being confidently wrong about what goes in which bin is this product's worst
  failure mode, and the mapping from shape to rule lives in a per-region pack
  that is versioned, sourced and separately reviewed.
- **Do not deploy this outside Deggendorf and expect the numbers above.** They
  are in-distribution numbers on one city, and the card says so twice because it
  is the most likely way to be wrong with them.
- **Do not treat a colour attribute as measured.** As of 2026-08-22 colour
  agreement against hand-labelled ground truth is **0.5625** on bin bodies, and
  lid colour is **not measured at all** — an upper-band sampler scored 0.1966 and
  was deliberately left unwired. Any rule matching on `lid_color` is unreachable.
- **Do not fine-tune on user-contributed images without human label review.**
  High-agreement machine labels may auto-accept over a public corpus; never over
  a frame a user contributed, and never for a form factor that has no data yet.

## Provenance

Trained on [`arudaev/smart-bin-detect`](https://huggingface.co/datasets/arudaev/smart-bin-detect)
at revision `8666aa23` — 18 954 frames: 370 legacy, 1 110 Open Images bins,
17 474 background. The identifier additionally uses a private crop pool whose
403 crops were **all** adjudicated by a human, blind, in a single pass; against
that pass the pool's shipped stream→shape proposals were wrong on **116 of
403 (28.8 %)**.

Splits are **group-aware on capture cluster**, so two photographs of the same
bin can never straddle a split.