SatellaDet-Blood

SatellaDet-Blood

SatellaDet-Blood is a public benchmark of the custom SatellaDet object-detection architecture on the TXL-PBC peripheral blood-cell dataset.

SatellaDet was originally developed for dense microbiological object detection.

This experiment tests whether the same detector architecture can generalise to a substantially different microscopy domain without redesigning the architecture specifically for blood-cell detection.

The model was trained from scratch at 640 × 640 resolution.

It detects three classes:

• WBC
• RBC
• Platelets

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Held-out test results

The checkpoint was selected using the validation split only.

After training and checkpoint selection were complete, best_score.pt was evaluated against the untouched official TXL-PBC test split.

Per-class held-out performance

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Selected validation checkpoint

The SatellaScore-selected checkpoint was obtained at epoch 64.

Validation and untouched test performance were closely aligned:

Metric       Validation     Test
mAP50          0.9378      0.9341
mAP50-95       0.7353      0.7346

The official test split was not used for checkpoint selection.

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SatellaScore

Checkpoint selection used equal class weighting.

This prevents the substantially more numerous RBC annotations from dominating checkpoint selection.

WBC        0.333333
RBC        0.333333
Platelets  0.333334

SatellaScore composition:

mAP50-95   35%
mAP50      25%
F1         30%
Count      10%

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Included files

Models

model/SatellaDet_Blood_TXL_PBC_640.onnx

Deployable ONNX export of the selected SatellaDet-Blood model.

model/best_score.pt

PyTorch checkpoint selected using SatellaScore.

Benchmark artifacts

The results/ directory contains:

• detection metric graphs
• training-loss graph
• count-error graph
• held-out test metrics
• held-out test summary
• sanitised training summary

PUBLIC_MANIFEST.json contains SHA-256 hashes and file sizes for the public release.

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Dataset

This benchmark uses the public TXL-PBC peripheral blood-cell dataset.

TXL-PBC contains:

• 1,260 microscopy images
• 18,143 annotated blood cells
• official training, validation, and test splits

Reference:

Gan, L., Li, X. & Wang, X.

TXL-PBC: a peripheral blood cell dataset with comprehensive annotations.

Scientific Data 12, 1694 (2025).

DOI:

10.1038/s41597-025-05980-z

Dataset repository:

https://github.com/lugan113/TXL-PBC_Dataset

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SatellaDet

SatellaDet is a custom object-detection architecture developed by Ephraim Asad.

Source repository:

https://github.com/EphraimAsad/SatellaDet

SatellaDet-Blood was trained from scratch rather than fine-tuned from a pretrained blood-cell detector.

The purpose of this experiment was to test whether the architecture could generalise beyond the microbiological colony-detection tasks for which it was originally developed.

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Privacy

This public release contains only:

• model weights
• aggregate benchmark metrics
• generated training graphs
• public model documentation

It intentionally excludes:

• raw microscopy images
• raw TXL-PBC dataset files
• private laboratory data
• training console logs
• environment dumps
• notebooks
• local configuration files
• API credentials
• user-specific filesystem information

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Intended use

SatellaDet-Blood is intended for:

• object-detection research
• computer-vision experimentation
• benchmarking
• architecture evaluation
• educational use

It is not a clinical diagnostic system.

It should not be used to make medical diagnoses or patient-care decisions.

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Benchmark note

The headline metrics in this repository correspond to the untouched TXL-PBC test split.

Because training settings and evaluation implementations can differ between detectors, comparisons with results published by other architectures should be treated as contextual unless evaluated under a matched experimental protocol.