Urban scene segmentation and cross-dataset transfer learning using SegFormer
Bibliographic record
Abstract
Semantic segmentation is essential for autonomous driving applications, but state-of-the-art models are typically evaluated on large datasets like Cityscapes, leaving smaller datasets underexplored. This research gap limits our understanding of how transformer-based models generalize across diverse urban scenes with limited training data. This paper presents a comprehensive evaluation of SegFormer architectural variants (B3, B4, B5) on the CamVid dataset and investigates cross-dataset transfer learning from CamVid to KITTI. Using an optimization framework combining cross-entropy loss with class weighting and boundary-aware components, our experiments establish new performance baselines on CamVid and demonstrate that transfer learning provides benefits when target domain data is limited. We achieve a modest 2.57% relative mean Intersection over Union (mIoU) improvement on KITTI through knowledge transfer from CamVid, along with 61.1% faster convergence. Additionally, we observe substantial class-specific improvements of up to 30.75% for challenging categories. Our analysis provides insights into model scaling effects, cross-dataset knowledge transfer mechanisms, and practical strategies for addressing data scarcity in urban scene segmentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".