---
license: other
license_name: mixed-cc-by-nc-4.0-and-mit
license_link: LICENSE.md
tags:
- cell-segmentation
- instance-segmentation
- weak-supervision
- microscopy
- mask-rcnn
- detectron2
library_name: detectron2
---

# wsbench: trained models

All trained models of **What Labels Are Worth Buying? Budget-Matched Weak Supervision for Cell Segmentation**
(Khalid, Naseem, Dengel, Ahmed; ISBI 2027, submitted). Code: https://github.com/nabeelkhalid92/wsbench
Labels and splits: https://huggingface.co/datasets/nabeelkhalid92/wsbench-labels

Every model is a Mask R-CNN R50-FPN (detectron2), trained with one label strategy at one annotation budget and one
seed, and selected on the validation set. Together they cover every strategy, budget and seed of the paper,
plus the ablations and baselines.

## Layout

```
<dataset>/<method>/<setting>/seed<k>/
  model_best.pth      weights (val-selected checkpoint)
  config.yaml         full detectron2 config used for training
  run_info.json       dataset, method, label file, best iteration, val score, training time
  val_history.json    validation score at every evaluation
  score_thr.json      score threshold chosen on val (for PQ / F1)
```

`index.json` lists all models.

| `<method>` | Strategy in the paper |
|---|---|
| `mrcnn_full` | **Mask**: full masks (`budget_mask_B<s>`), or all training masks (`mask`: the "Full, 6.6 B1" reference) |
| `ours` | **Box** (`budget_box_B<s>`) and **Mixed** (`budget_mixed_B<s>`): SAM2.1 pseudo-labels with the verification score |
| `pointsup` | **Box+1pt** (`budget_pts1_B<s>`): one labelled point per box (PointSup-based Point2Mask) |
| `mrcnn_boxrect` | box used directly as the mask (floor) |
| `boxinst`, `discobox`, `boxsnake`, `boxteacher` | box-supervised baselines (their own architectures) |

`B<s>` is the annotation budget in seconds (B1 = time to box every training cell: yeast 7826 s, DSB2018 164605 s,
LIVECell 7130032 s). LIVECell settings with a cell-type suffix (`_skbr3`, `_bv2`, `_shsy5y`) are the single-cell-type
study. Other names: `mixednocal` = Mixed without calibration, `*_selsingle` / `*_seltoppred` / `*_acc0_noweight` =
pseudo-label selection ablations, `mask_<celltype>` = all training masks of one cell type (reference).
`pts1sam` = **SAM+pt**: a box and one labelled point per cell; the box prompts SAM2.1 and the point is used to choose
among its masks (and as a point loss for cells whose mask is rejected). These models were trained after
a fix to the point loss on rejected cells (code commit 2e635fa, 29 Oct 2026); earlier versions of
`yeast/ours/budget_pts1sam_B490lr/seed0-1` were replaced. The LIVECell ablation `boxpts7_sam2l_subset600`
(7 points per cell) was trained before this fix.

## Use

```python
from huggingface_hub import hf_hub_download
ckpt = hf_hub_download("nabeelkhalid92/wsbench-models", "yeast/ours/budget_box_B1960/seed0/model_best.pth")
cfg = hf_hub_download("nabeelkhalid92/wsbench-models", "yeast/ours/budget_box_B1960/seed0/config.yaml")
```

With the code repository: `python wsbench/train/train_net.py --dataset yeast --method ours --setting budget_box_B1960
--seed 0 --train-json <label file> --predict-only` after placing the folder under `workspace/runs/`.
Inputs follow the converted datasets of the code repository (yeast and DSB2018 images are 8-bit, see its converters).

## Licence

Models trained on **LIVECell** (`livecell/`) are derived from LIVECell (CC BY-NC 4.0): **non-commercial use only**,
with attribution to Edlund et al., Nat. Methods 2021. Models trained on **yeast in microstructures** (`yeast/`,
MIT, Reich et al. 2023) and **DSB2018 / BBBC038** (`dsb2018/`, CC0, Caicedo et al. 2019) are released under the
MIT licence. See `LICENSE.md`.

## Citation

```bibtex
@inproceedings{khalid2027labels,
  title     = {What Labels Are Worth Buying? Budget-Matched Weak Supervision for Cell Segmentation},
  author    = {Khalid, Nabeel and Naseem, Khola and Dengel, Andreas and Ahmed, Sheraz},
  booktitle = {Proc. IEEE International Symposium on Biomedical Imaging (ISBI)},
  year      = {2027},
  note      = {submitted}
}
```