INDUSTRY FIELD GUIDE
Recycling sortation data for AI material recognition
A recycling model has to recognize materials when packaging is dirty, crushed or partly hidden.
NAICS 562920 · Materials Recovery Facilities
A recycling model has to recognize materials when packaging is dirty, crushed or partly hidden. AMP’s product documentation shows a commercial use of computer vision in waste sorting. It does not show that AMP is buying outside footage. A facility considering a data project can propose a bench collection around a defined classification or error. AMP sorting technology
Give the model a real ambiguity
Use approved clean material samples on a stationary table. Stage a transparent container under glare, a crumpled item with a label removed and two visually similar items that should enter different local streams. Record the verified material class and the local rule that governs the routing. Annotate overlaps and conditions where the human reviewer cannot identify an item. Keep examples of uncertain labels separate.
Hold back complete object instances rather than random frames from the same video. If the buyer needs conveyor footage, agree separately on camera position, belt speed, lighting and whether an item’s material is independently confirmed.
Stay outside active sorting equipment
Use authorized training materials and a controlled location. Keep workers, customer addresses on packaging, proprietary layout and operational telemetry outside the initial sample. Do not create unsafe exposure to hazardous or contaminated waste for a recording.
Describe the stream and decision. Include which material classes you can verify, what equipment is out of scope and what a buyer can measure from the sample. Describe collection capabilities
Original Fiund exercise
Verified material and a local stream rule can disagree with a visual shortcut
Owned clean training objects on a stationary table use original annotations and an invented routing rule. There is no conveyor, hazardous waste, actual facility rule or claim of buyer demand.
| Record | Source | What it records |
|---|---|---|
| RC1 | Local practice rule | Verified material PLASTIC goes to stream P; verified GLASS to G. Unidentified objects route to REVIEW. Rule is original to this fixture. |
| RC2 | Objects A and B | Transparent A has verified plastic provenance, glare partly obscures its shape. Similar transparent B has verified glass provenance. Distinct full object IDs. |
| RC3 | Object C and split draft | C is crumpled with removed label; reviewer cannot verify material. Draft training/test split uses adjacent frames of A in both sets. |
Question for the model
Which streams follow from the verified labels, what happens to C, and why does the proposed split weakly test new-object generalization?
Show the reference answer and checks
RC1 and RC2 route verified plastic A to P and glass B to G despite their similar transparent appearance. RC3's C lacks a verified material label and belongs in REVIEW rather than a forced class. Adjacent frames of A in both training and test reuse the same object and are a weak test of transfer to unseen instances. Hold out complete separately verified objects and preserve uncertainty instead of using transparency as a universal material shortcut.
- Inferring material solely from transparency or a familiar shape.
- Forcing an uncertain class or claiming new-object transfer from adjacent frames of the same object.
Fields to preserve
- Object ID and verified material source
- Defines ground truth.
- Local rule, stream and uncertainty
- Supports routing.
- Occlusion and instance-level split
- Preserves capture and evaluation limits.
Test another case
Use an original opaque plastic object and a clear nonmatching prop with independently reviewed labels. Test material evidence rather than the initial appearance pattern.
Use approved clean objects away from operating equipment. Exclude workers, addresses, layouts and telemetry; no contaminated material is needed.
Discuss a contribution
Describe the sources you control, the task they could support and what has already been checked. Use the existing-data assessment for an archive or the collection brief for a new recording project. The contributor agreement defines Fiund’s licensing authority and compensation. A guide or fictional exercise does not establish an accepted collection or a sale.