Datasets:

ParsaVictor
/

pcb-component-detection

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label pcb-component-detection@22d3f91bc7aac41a0ec6737bf744896207d9d5e1
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2572, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2489, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2244, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1518, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1541, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1210, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1147, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1168, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label pcb-component-detection@22d3f91bc7aac41a0ec6737bf744896207d9d5e1

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PCB Component Detection Dataset (High-Resolution)

A YOLO-format object detection dataset for locating and classifying electronic components on a printed circuit board — resistors, capacitors, ICs, diodes, connectors, and more — from full high-resolution board photographs.

This dataset powers ParsaVictor/pcb-component-detection-yolo26, part of the PCB Inspection Suite.

Why high-resolution matters

A naive 640×640 export (the Roboflow default) shrinks small components like resistors down to a handful of pixels, which caps detector accuracy hard. This dataset was rebuilt from original full-resolution board photographs (median part size ~19px at the chosen tiling, vs ~6px in the original low-res export), with labels carefully re-mapped back onto the originals.

Classes (13)

Class	# instances
Resistor	34,470
Capacitor	33,923
IC	12,015
Electrolytic Capacitor	10,508
Transistor	4,049
Diode	3,904
Inductor	3,402
Connector	2,867
Marker	2,465
Other	1,680
LED	444
Switch/Button	287
Oscillator	268

Splits

Split	Images
train	3,005
val	321
test	292

Images are tiled crops from full-resolution board photographs (1024px tiles, 25% overlap) so components stay detectable at training resolution while the full board can still be reconstructed at inference time.

Format

Standard YOLO format:

images/{train,val,test}/*.jpg
labels/{train,val,test}/*.txt   # class_id x_center y_center width height (normalized)
data.yaml

A simplified binary "any component" variant (single class, same images) is also used internally as a first detection stage before fine-grained classification; see the suite overview for the two-stage pipeline.

Quick start

from ultralytics import YOLO

model = YOLO("yolo26s.pt")
model.train(data="data.yaml", imgsz=1024, epochs=80)

from huggingface_hub import snapshot_download
path = snapshot_download(repo_id="ParsaVictor/pcb-component-detection", repo_type="dataset")

Sibling dataset: pcb-bare-board-defect-detection
Sibling dataset: pcb-solder-defect-detection
Full suite overview: pcb-inspection-suite

License / attribution

Released for research and development use. Built from manually re-annotated, full-resolution board photographs. Contact the author before any commercial redistribution. See LICENSE.md.