A deep learning computer vision iPad application for Sales Rep optimization in the field
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Computer vision is becoming an increasingly critical area of research, and its applications to real-world problems are gaining significance. In this paper, we describe the design, development and evaluation of our computer vision Faster R-CNN iPad App for Sales Representatives in grocery store environments. Our system aims to assist Sales Reps to be more productive, reduce errors, and provide increased efficiencies. We report on the creation of the iPad app, the data capturing guidelines we created for the creation of good classifiers and the results of professional Sales Reps evaluating our system. Our system was tested in a variety of conditions in grocery store environments and has an accuracy of 99%, a System Usability Score usability score of 85 (high). It supports up to 40 classifiers running concurrently to perform product identification in less than 3.8 s. We also created a set of data capturing guidelines that will enable other researchers to create their own classifiers for these types of products in complex environments (e.g., products with very similar packaging located on shelves).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it