ApesNet: a pixel‐wise efficient segmentation network for embedded devices
Bibliographic record
Abstract
Road scene understanding and semantic segmentation is an on‐going issue for computer vision. A precise segmentation can help a machine learning model understand the real world more accurately. In addition, a well‐designed efficient model can be used on source limited devices. The authors aim to implement an efficient high‐level, scene understanding model in an embedded device with finite power and resources. Toward this goal, the authors propose ApesNet, an efficient pixel‐wise segmentation network which understands road scenes in near real‐time and has achieved promising accuracy. The key findings in the authors’ experiments are significantly lower the classification time and achieving a high accuracy compared with other conventional segmentation methods. The model is characterised by an efficient training and a sufficient fast testing. Experimentally, the authors use two road scene benchmarks, CamVid and Cityscapes to show the advantages of ApesNet. The authors’ compare the proposed architecture's accuracy and time performance with SegNet‐Basic, a deep convolutional encoder–decoder architecture. ApesNet is 37% smaller than SegNet‐Basic in terms of model size. With this advantage, the combining encoding and decoding time for each image is 2.5 times faster than SegNet‐Basic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".