CVNodes: A Visual Programming Paradigm for Developing Computer Vision Algorithms
Bibliographic record
Abstract
Advances in machine learning have led to a rapid pace of innovation in Computer vision and deep learning classification algorithms. Deep learning classification models are often limited in flexibility due to their fixed pre-processing steps embedded into the algorithm and lack ways to easily iterate, debug, and analyze developed algorithms without programming knowledge. The lack of high-level tools for developing vision algorithms leads to longer development times that require significant knowledge of underlying algorithms. What about individuals without this deep knowledge of machine learning and vision yet wish to develop algorithms to prototype ideas? What about nonprogrammers such as designers and artists that wish to utilize the state-of-the-art in computer vision in their work? To address this under-served community, we propose a visual-programming solution akin to those found in modern game engines geared towards computer vision algorithm development. These results in a new prototyping tool to empower researchers and nonprogrammers to easily iterate algorithm development, use pretrained classification models, and provide statistical post-analysis tools.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".