Fusion of Ensembled UNET and Ensembled FPN for Semantic Segmentation
Bibliographic record
Abstract
Image segmentation is an annotation method used to gain a deeper understanding of the images.Semantic segmentation involves constructing a pixel-by-pixel mask of an image by training a neural network.The accuracy of the semantic segmentation algorithms can be improved by eliminating background noise, and computational efficiency can be improved by using the pre-trained networks.This paper proposes a new architecture that ensemble inceptionV3, DenseNet, Resnet34 in the encoder part of UNET and ensemble inceptionV3, Resnet34, and VGG16 in the encoder part of FPN.The ensemble results are fused based on the weighted average and the predictions of the pixels are made on the fused features to perform semantic segmentation.The proposed architecture is implemented on Oxford-IIIT Pet Dataset, created by the visual geometry group, and on the SD saliency 900 dataset.The F1 score, IOU score, and Loss are used to evaluate segmentation model results.The results of the study show that the proposed architecture formed by the fusion of ensembled architectures is more accurate and efficient in segmenting oxford-IIIT pet dataset with the IoU score of 98.68% and segmenting the SD saliency 900 dataset with the IoU score of 66.78%.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".