Datasets:

Ahnuf
/

Military_Aircraft_Detection_Dataset

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label Military_Aircraft_Detection_Dataset@29961372553c79ddc6fc0a9cba1f2ba6c176f17e
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2092, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2197, in cast_table_to_features
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1995, in cast_array_to_feature
                  return feature.cast_storage(array)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1169, in cast_storage
                  [self._strval2int(label) if label is not None else None for label in storage.to_pylist()]
                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1098, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label Military_Aircraft_Detection_Dataset@29961372553c79ddc6fc0a9cba1f2ba6c176f17e

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Military Aircraft Detection & Classification Dataset (87 Classes)

Overview

This dataset is a professionally prepared resource for training high-performance object detection models like YOLO and classification models like EfficientNet-B4. It features a balanced distribution across 87 distinct military aircraft classes, specifically refined to eliminate ultra-rare samples and prevent model bias.

Key Technical Specifications

Total Images: 25,894.
Resolution: Uniform 640x640 pixels.
Annotation Format: YOLO-Ready (.txt) with normalized coordinates.
Negative Samples: Included 10% background-only images (2,353 images) to significantly reduce false positives in empty skies.

Understanding the Annotation Format

Each image has a matching .txt file containing the detection labels.

Positive Sample Example

A file named su57_01.txt containing: 68 0.475000 0.496875 0.415625 0.859375

68: Class ID. Matches Su57 in our 87-class table.
0.475000: X-Center. Horizontal center at 47.5% of image width.
0.496875: Y-Center. Vertical center at 49.6% of image height.
0.415625: Width. Bounding box spans 41.5% of image width.
0.859375: Height. Bounding box spans 85.9% of image height.

Negative Sample (Background) Example

10% of the dataset consists of background images to prevent "ghost" detections.

File: sky_bg_01.txt
Content: Empty (0 bytes)
Purpose: Teaches the model that no aircraft are present in this specific image.

Final Class ID Table (87 Classes)

ID	Class	ID	Class	ID	Class	ID	Class
0	A10	22	CL415	44	JF17	66	Su34

1	A400M	23	E2	45	JH7	67	Su47

2	AG600	24	E7	46	KAAN	68	Su57

3	AH64	25	EF2000	47	KC135	69	TB001

4	AKINCI	26	EMB314	48	KF21	70	TB2

5	AV8B	27	F117	49	KJ600	71	Tejas

6	An124	28	F14	50	Ka27	72	Tornado

7	An22	29	F15	51	Ka52	73	Tu160

8	An225	30	F16	52	MQ9	74	Tu22M

9	An72	31	F18	53	Mi24	75	Tu95

10	B1	32	F2	54	Mi26	76	U2

11	B2	33	F22	55	Mi28	77	UH60

12	B52	34	F35	56	Mi8	78	US2

13	Be200	35	F4	57	Mig29	79	V22

14	C1	36	FCK1	58	Mig31	80	Vulcan

15	C130	37	H6	59	Mirage2000	81	WZ7

16	C17	38	Il76	60	P3	82	X32

17	C2	39	J10	61	RQ4	83	XB70

18	C390	40	J20	62	Rafale	84	Y20

19	C5	41	J35	63	SR71	85	YF23

20	CH47	42	J36	64	Su24	86	Z10

21	CH53	43	JAS39	65	Su25	87	Z19

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