Leveraging Prompt-Based Segmentation Models and Large Dataset to Improve Detection of Trees
Bibliographic record
Abstract
The abundance of unlabeled forest images on the web is a powerful yet untapped resource to train forestry vision models. Two key challenges limiting the use of these unlabeled images are i) collecting the images and ii) obtaining the labels, as supervised learning remains the prevailing approach for model training. In this work, we address the first issue by providing a dataset of 110 k forest images sourced from a repository of pictures taken by amateur photographers worldwide. To generate supplementary labels for supervised training, we propose a two-step approach. First, we train a network on a small labelled dataset, to generate pseudo-labels on the much larger, unlabeled one. Then, we leverage the zero-shot segmentation capability of the Segment Anything Model to improve the quality of these pseudo-labels. Our experiments demonstrate that both the proposed dataset and the pseudo-labeling method increase performance of a tree detector at no additional labeling cost. This performance increase is particularly significant in challenging scenarios, showing that training the model with better segmentation masks notably helps disentangle overlapping trees and detect odd-shaped ones, gaining between 3.3 APbb, 7.7 APseg or 1.6 APbb, 3.5 APseg percentage points depending on the burn-in model. Code and dataset links are available at https://github.com/norlab-ulaval/PercepTreeV1 .
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".