Cytellect / README.md
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Clarify model description and organize evaluation details
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---
license: bsd-3-clause
tags:
- microscopy
- image-segmentation
---
# Cytellect
Cytellect provides fine-tuned Cellpose-SAM weights for nucleolar segmentation in microscopy images. The models build on the `cpsam_v2` checkpoint and are distributed with checkpoint hashes, processing recipes and evaluation metadata for reproducible use.
This repository includes checkpoints at 750 and 375 training updates. Fine-tuning used human-reviewed microscopy annotations, with no synthetic images.
## Model checkpoints
| Version | File | SHA256 |
|---|---|---|
| 750 updates | `ncl/r1-step0750.pt` | `9d2c2ec06aa1aa71f36695b49b0828f82fd3cb633da2832c99a9f194d0bd4099` |
| 375 updates | `ncl/r1-step0375.pt` | `bc5ea7f25ce9a9f96e9ff510cee20587e1328ac834a0f7f82aee0bc06156d906` |
The corresponding JSON files contain the fixed recipe, package versions, provenance hashes and aggregate metrics. The training images use NCL staining.
## Evaluation
The following development-set comparison uses the same reference annotations, fixed local product preprocessing, parent-nucleus gating and postprocessing for all checkpoints. Instance matching uses an IoU threshold of 0.5.
| Product output | TP | FP | FN | Instance F1 | Union Dice | Total area difference |
|---|---:|---:|---:|---:|---:|---:|
| Untouched baseline | 3 | 4 | 1 | 0.5455 | 0.8534 | -20.38% |
| 375 updates | 4 | 2 | 0 | 0.8000 | 0.9349 | -2.69% |
| 750 updates | 4 | 0 | 0 | 1.0000 | 0.8951 | -11.70% |
The 750-update checkpoint achieves the highest instance F1 in this comparison; the 375-update checkpoint has the highest union Dice and closest total area agreement. These metrics describe complementary aspects of detection and segmentation.
<details>
<summary>Evaluation data and measurement details</summary>
Training used 8 human-approved objects in 2 regions from one experiment. Development used 4 reference objects in one region from a separately cultured and treated experiment. The development image was excluded from training and used for checkpoint selection. The reported results are development-set measurements, not an independent final-test estimate. Training-region results are excluded from the comparison.
At 750 updates, three objects have area errors of +5, -6 and +5 pixels. The fourth object has 360 predicted pixels versus 608 reference pixels, corresponding to 40.8% undersegmentation and IoU 0.5638. Instance F1 of 1.0 represents four matches at IoU 0.5; at IoU 0.7, instance F1 is 0.75. Boundary and area agreement are reported separately from instance detection.
Raw model output at 750 updates has TP 3, FP 1, FN 1 and F1 0.75. The fourth object has raw IoU of approximately 0.428; fixed product postprocessing raises it above 0.5. The table above reports the processed output. Relative to 375 updates, the 750-update checkpoint removes two unmatched fragments, with lower union Dice and a larger total area difference. Training-region inspection also recorded geometry errors and a downstream parent-gate exclusion.
The results describe the stated images and processing conditions. Performance on other staining protocols and experiments should be evaluated using corresponding reference annotations.
</details>
## Usage and reproducibility
Use the input conditions and processing recipe documented in the accompanying metadata. Preserve the base model and record the exact checkpoint identity. Load these state dictionaries with a compatible Cellpose-SAM implementation and `weights_only=True`.
The repository distributes model state-dict weights and aggregate metadata. Research images, annotations, optimizer state, raw logs, specimen identifiers and private filesystem paths are excluded.
## License and attribution
The upstream fixed-revision model card declares BSD-3-Clause. See `NOTICE.md` for the exact upstream revision and base checkpoint, and `LICENSE.cellpose` for the upstream copyright, conditions and disclaimer. The weights have been modified by fine-tuning; no upstream endorsement is implied.