Quantitative Analysis of Deep Learning-Based Object Detection Models
Bibliographic record
Abstract
The rise of convolutional networks in computer vision, especially for generic object detection, has led to the emergence of a myriad of efficient and precise object detection models. Typically, deep learning-driven object detectors operate in two phases: initially, they utilize convolutional networks to extract compact feature embeddings from images; subsequently, these embeddings are used to pinpoint localized object positions. Rooted in convolutional networks, these generic object detection models have the capability to learn from vast datasets that comprise hundreds of thousands of images with thousands of objects. This vast data training gives them unparalleled generalization capabilities, setting them apart from traditional methods. With the swift pace of research, new object detection models are frequently unveiled, each striving for state-of-the-art performance on renowned benchmarks. Given the abundance of viable models, selecting the optimal one can be a daunting task. In this paper, we offer a succinct overview of widely recognized object detectors, emphasizing their architectural distinctions, and presenting a quantitative comparison in terms of accuracy and inference speeds using the popular 2017 Common Objects in Context dataset.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".