Effectiveness of Teachable Machine, mobile net, and YOLO for object detection: A comparative study on practical applications
Bibliographic record
Abstract
In this comparative study, the effectiveness of three prominent object detection models—Teachable Machine, MobileNet, and YOLO—was evaluated using a diverse dataset consisting of images from four distinct categories: bird, horse, laptop, and sandwich. The objective was to identify the most efficient model in terms of accuracy, speed, and usability for practical applications in fields such as self-driving vehicles, robotics, security systems, and augmented reality. The dataset was meticulously curated and subjected to training across the three models. Results from the comprehensive analysis indicated that the Teachable Machine model surpassed both MobileNet and YOLO in performance, demonstrating superior accuracy and effectiveness in detecting objects across the specified categories. This research contributes significantly to the domain of artificial intelligence by providing detailed insights and comparisons of model performances, offering a valuable resource for further advancements in object detection technologies. The study not only showcases the Teachable Machine’s superiority in handling multi-class object detection problems but also sets a benchmark for future explorations in enhancing object detection methodologies.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".