Trim the dataset card

README.md

@@ -82,30 +82,6 @@ occur in the use case, so some sessions deliberately contain them as noise (`pol
 - Cups for the kernels, each holding a verified 50 kernels
 - Printed session cards and special cards (REDO, INVALIDATE) from the planning script
 
-### Procedure
-
-Each session covers one species and one pollution level, and
-follows a pre-generated plan of about 30 session cards. Each card carries a QR code and
-states the new total and what to add or remove (`add 17`, `remove 12`, `add 1 cup + 9`).
-
-1. Take the next session card and put the stated number of kernels on the A4 sheet.
-2. **If the card says CHECK:** recount the kernels.
-   - The count matches the large number on the card: continue.
-   - It does not: photograph an *INVALIDATE* card and fix the pile until it matches the
-     card.
-3. Photograph the session card.
-4. Spread the kernels on the sheet so that no piles remain; touching is fine.
-5. Photograph the layout twice per phone, with the whole A4 sheet in frame:
-   first roughly top-down, then tilted or from another distance.
-6. Next card.
-
-If something goes wrong within a card, a *REDO* card is photographed, the pile is fixed to
-the number on the current card, and the card and the layout are photographed again.
-
-### Variation between sessions
-
-Phones (see the table above) and backgrounds (table, mat, tray, floor).
-
 ### Sessions
 
 - **S02:** spring oats (`oats-s`), not polluted; Galaxy S23 and iPhone 8;
@@ -119,16 +95,6 @@ Phones (see the table above) and backgrounds (table, mat, tray, floor).
 - **S05:** winter durum wheat (`durum-w`), lightly polluted; Galaxy S23 (shot as
   DNG) and iPhone 8; 2026-10-15.
 
-### Labelling
-
-A script decodes the card photos and assigns their total to the photos that follow them.
-It checks that every total follows from the previous one and the card's delta. A group
-whose chain breaks (a skipped or unread card) stays *suspect* until the next CHECK, and
-groups before an INVALIDATE are *invalidated*. **Only groups whose chain validated are in
-this dataset**, after a review of every group by eye. 
-
-Card photos are not included.
-
 ## Splitting
 
 Images are **not independent**: every arrangement appears in 2–22 photos (several phones,
@@ -167,25 +133,6 @@ a kernel was missed or lies at the edge of the frame. `count` stays the label.
 - Some sessions contain pollution (`pollution` ≠ `none`). It is visible in the photos and
   deliberately not part of the count.
 
-## Contributing
-
-New sessions are welcome. **Do not upload photos to this repository directly.** Every file
-here is generated by the export script from reviewed labels; photos without a
-`metadata.jsonl` row have no count and are useless for training. The tools are in the
-[project repository](https://github.com/SmartFarmingLab/corn1000) (private, access on
-request from the Smart Farming Lab).
-
-1. **Plan:** `tools/plan_session.py <SESSION> --species … --pollution …`
-   writes `sessions/<SESSION>/plan.json` and the cards to print. Keep `plan.json`: it is
-   the reference the labels are checked against.
-2. **Shoot** the session as described under [Procedure](#procedure).
-3. **Label:** copy the original files (not via a messenger) to `sessions/<SESSION>/` and run
-   `tools/sort_photos.py sessions/<SESSION> --out labels/<SESSION>`.
-4. **Review** every group in `labels/<SESSION>/review.html` and mark the checked rows
-   `review=done` in `labels.csv`.
-5. **Hand in** `plan.json`, `labels/<SESSION>/` and the original photos to the maintainers
-   (the photos are too large for git). They run the export and upload the session here.
-
 ## License
 
 [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/): free to use and adapt for