The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: '<datasets.utils.file_utils.FilesIterable object at 0x7f6634442270>'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/xml/xml.py", line 67, in _generate_tables
with open(file, encoding=self.config.encoding, errors=self.config.encoding_errors) as f:
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/streaming.py", line 73, in wrapper
return function(*args, download_config=download_config, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 967, in xopen
return open(main_hop, mode, *args, **kwargs)
FileNotFoundError: [Errno 2] No such file or directory: '<datasets.utils.file_utils.FilesIterable object at 0x7f6634442270>'Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Annotated-Digital-LCD-Utility-Meter-And-Serial-Numbers-Dataset-Sample
DS_electro_scor_ser_labeled_v1_2026 — Free 50-File Sample
Read the consumption value on the LCD screen and the factory serial number on the casing in a single inference pass — and bind usage data straight to the right customer account, with no manual matching.
This repo is a free preview: the first 50 annotated kits from the full dataset. Download them, drop them into your training pipeline, and check the annotation quality before you buy anything.
Dataset Summary
| Kits included (this sample) | 50 (image + YOLO .txt + PASCAL VOC .xml) |
| Full dataset | 2,290 expertly labeled kits |
| Classes | 25, context-isolated |
| Resolution | 640 px, VRAM-optimized |
| Data origin | 100% real, non-synthetic, "in-the-wild" |
Dataset Structure
DS_electro_scor_ser_labeled_v1_2026_sample/
├── Images/ # JPG photos of utility meters
├── Labels_YOLO/ # normalized relative coordinates (.txt)
├── Labels_PASCAL_VOC/ # absolute pixel coordinates (.xml)
├── classes.txt # 25-class master list
└── README_Documentation.txt
Pre-formatted for Ultralytics and standard ML pipelines — clone, point your config at it, start training.
Data Fields — 25-class context-isolated taxonomy
- On-screen metrics (IDs 0–9) — digits read directly off the LCD register.
- Serial verification — a separate character space (
s0…s9,s/,s_) for hardware serial numbers. - Target zones —
comma,scoreboard(the LCD window), andsa(the serial box), for region-first, decode-second pipelines.
Keeping on-screen digits and stamped hardware characters in separate class blocks prevents the model from confusing an LCD digit with a plastic-embossed serial character.
Why This Data Trains Better Models
- Flawless context separation. Distinct classes for on-screen metrics vs. hardware text stop the model from conflating a glowing LCD digit with a stamped serial character.
- True in-the-wild resilience. Labels explicitly cover glass and display glare, low-contrast screens in unlit basements, harsh viewing-angle distortion, faded LCD segments, and weathered or tiny serial prints.
- Manufacturer diversity. Real assets from 100+ European brands across electricity, heat, water, and gas smart meters, so models generalize instead of overfitting to one vendor.
- Training-efficient by design. Standardized 640 px resolution keeps small serial fonts legible while cutting GPU overhead.
Full Dataset & Bonus Model Weights
This sample covers 50 of 2,290 total kits. The full, production-grade dataset — plus a bonus pre-trained neural network trained on these 25 context-isolated classes (mAP50-95: 94%) — is available at:
👉 https://utilitymeters.ai/datasets-utility-meters/dataset-lcd-counters
Related Datasets from UtilityMeters.ai
UtilityMeters.ai specializes exclusively in utility-meter datasets — fully annotated sets and raw unlabeled photos for teams that prefer to label in-house:
- Mechanical dials only · LCD screens only · hybrid mechanical + digital mix
- Dials + serial numbers, for full automated asset tracking
- 20,000+ raw images, from $0.35/image, minimum order 1,000
Browse everything: https://utilitymeters.ai
Licensing Information
Datasets (images & annotations) ship with full commercial rights for training proprietary ML/AI applications. Pre-trained models are supplied as a free bonus under GNU AGPL-3.0. Redistribution or resale of raw files is strictly prohibited. Full terms on the product page.
Contact
Built for ML engineers and utility companies building autonomous billing and asset-tracking infrastructure. Questions or a custom collection request: https://utilitymeters.ai
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