Datasets:

LibreYOLO
/

ap10k-pose

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label ap10k-pose@f0e6c82c249602b81f72cb95d0bfbe0c16fcd3b5
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 478, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, 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 2285, 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 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, 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 1469, 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 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label ap10k-pose@f0e6c82c249602b81f72cb95d0bfbe0c16fcd3b5

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AP-10K Animal Pose (LibreYOLO)

Multi-class animal pose in LibreYOLO YOLO-pose layout: detect and classify each animal into one of 54 species while predicting a single shared 17-keypoint quadruped skeleton. Hosted so model.train(...) / model.val(...) can consume it directly for multi-class pose (one kpt_shape for every class).

Provenance

Source: AP-10K, A Benchmark for Animal Pose Estimation in the Wild (Yu et al., NeurIPS 2021 Datasets and Benchmarks Track). Upstream: https://github.com/AlexTheBad/AP-10K (canonical release, Google Drive labeled set).
Pinned source sha256: 420980abb135d6f66bcc8e29f289a46081214016192ae197ad24bc1525c8e62c (the upstream ap-10k archive).
Transform applied: extracted the archive; converted the COCO-style keypoint split (split1) to YOLO-pose TXT, writing one class per species over the shared 17-keypoint skeleton; copied images into images/{train,val}; generated ap10k-pose.yaml (kpt_shape, flip_idx, skeleton, oks_sigmas). No third-party repackaging used.

ap10k-pose/
├── images/train/*.jpg   (7023)
├── images/val/*.jpg     (995)
├── labels/train/*.txt   (7023)   # <cls> <cx> <cy> <w> <h> (<x> <y> <v>)*17, normalized
├── labels/val/*.txt     (995)
└── ap10k-pose.yaml

Splits (AP-10K split1): 7,023 train images / 9,122 instances, 995 val images / 1,272 instances.
Classes: 54 species across 23 families (Bovidae, Canidae, Castoridae, Cercopithecidae, Cervidae, Cricetidae, Elephantidae, Equidae, Felidae, Giraffidae, Hippopotamidae, Hominidae, Leporidae, Mephitidae, Muridae, Mustelidae, Procyonidae, Rhinocerotidae, Sciuridae, Suidae, Talpidae, Ursidae, Vespertilionidae). Class index = species, ordered by upstream category id (see names in the yaml).
Keypoints (17, shared): left_eye, right_eye, nose, neck, root_of_tail, left/right shoulder, elbow, front_paw, left/right hip, knee, back_paw.

Use with LibreYOLO

from libreyolo import LibreYOLONAS
model = LibreYOLONAS("yolo_nas_pose_s_coco_pose.pth", size="s", task="pose")
model.train(data="ap10k-pose.yaml", epochs=100, imgsz=640)
model.val(data="ap10k-pose.yaml")

License

Source license: CC BY 4.0 (Creative Commons Attribution 4.0 International), inherited. Please attribute AP-10K:

Hang Yu, Yufei Xu, Jing Zhang, Wei Zhao, Ziyu Guan, Dacheng Tao. "AP-10K: A Benchmark for Animal Pose Estimation in the Wild." NeurIPS 2021 Datasets and Benchmarks Track. https://github.com/AlexTheBad/AP-10K

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Total file size:

2.78 GB

AP-10K Animal Pose (LibreYOLO)

Provenance

Contents

Use with LibreYOLO

License