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UFPR-AMR
This dataset contains 2,000 images taken from inside a warehouse of the Energy Company of Paraná (Copel), which directly serves more than 4 million consuming units in the Brazilian state of Paraná.
The images were acquired with three different cameras and are available in the JPG format with a resolution between 2,340 × 4,160 and 3,120 × 4,160 pixels. The dataset is split into three sets: training (800 images), validation (400 images) and testing (800 images).
Every image has the following annotations available in a text file: the camera in which the image was taken, the counter’s position (x,y,w,h) and reading, as well as the position of each digit. All counters of the dataset (regardless of meter type) have 5 digits, and thus 10,000 digits were manually annotated.
Source: Convolutional Neural Networks for Automatic Meter Reading
Benchmarks archive 2025-07-28
All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Meter Reading | UFPR-AMR | Fast-YOLOv2 + CR-NET Rank-1 Recognition Rate 94.13 | Convolutional Neural Networks for Automatic Meter Reading | — | 3 | Compare |
Papers archive 2025-07-28
1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 2. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Convolutional Neural Networks for Automatic Meter Reading | 0 | 3 | 25 Feb 2019 | not harvested |
Dataset loaders archive 2025-07-28
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License archive 2025-07-28
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Variants archive 2025-07-28
- UFPR-AMR
1 variant name, as the archive lists them.
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