SegEv: semantic segmentation performance verification and evaluation software
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the widespread application of semantic segmentation technology in fields such as remote sensing and industrial inspection, the evaluation of model performance and visualization of training processes have become key issues. This paper develops an integrated evaluation software based on PyQt5 and TensorBoard, which supports the calculation of eight metrics including Precision, Recall, F1, Accuracy, mPA, mIoU, Dice, ROC, and PR and provides functions such as multi-algorithm comparison and batch processing. Through TensorBoard, the software enables the visualization of model architectures, feature maps, heatmaps, and loss maps, intuitively displaying the differences between segmentation results and ground truth labels to assist in parameter optimization. With its modular design, the software combines both evaluation and visualization capabilities, providing efficient tool support for the development and deployment of segmentation models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it