| --- |
| 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, both given to SAM2.1. 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} |
| } |
| ``` |
| |
