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 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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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".