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Datasets:
raindrazinfg
/
ggpoker-annotations

Tasks:
Object Detection
Languages:
Chinese
English
Tags:
ggpoker
annotation
yolo
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
export_id: string
export_time: string
source_annotation_revision: string
label_schema_version: string
target_dataset_repo: string
target_dataset_revision: string
counts: struct<images: int64, labels: int64>
vs
version: string
flags: struct<>
shapes: list<item: struct<label: string, shape_type: string, flags: struct<>, points: list<item: list<item: double>>, group_id: null, description: null, difficult: bool, attributes: struct<>>>
imagePath: string
imageData: null
imageHeight: int64
imageWidth: int64
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                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 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 572, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              export_id: string
              export_time: string
              source_annotation_revision: string
              label_schema_version: string
              target_dataset_repo: string
              target_dataset_revision: string
              counts: struct<images: int64, labels: int64>
              vs
              version: string
              flags: struct<>
              shapes: list<item: struct<label: string, shape_type: string, flags: struct<>, points: list<item: list<item: double>>, group_id: null, description: null, difficult: bool, attributes: struct<>>>
              imagePath: string
              imageData: null
              imageHeight: int64
              imageWidth: int64

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标注仓库 README（模板）

本仓库用于管理标注过程数据与导出结果（非训练集本体）。

目录结构

  • meta/
    • classes.txt
    • label_schema.yaml
    • sources.yaml
    • splits.yaml
  • raw/
    • frames/
    • videos/
  • prelabel/
    • yolo/
  • tasks/
    • batch_0001/
      • images/
      • labels/
      • notes.md
  • curated/
    • hard_cases/
    • missing_candidates/
    • predictions/
    • pred_vis/
    • predict_image/
  • exports/
    • export_v001/
      • images/
      • labels/
      • export_meta.json

标注输入与输出

输入（标注前）：

  • 待标注图片放入 tasks//images/
  • 预标注（可选）放入 prelabel/yolo/ 与图片同名

输出（标注后）：

  • 标注结果（真值）输出到 tasks//labels/
  • 导出给训练集的结果输出到 exports//{images,labels}/

推荐最小流程

  1. 选样本：raw/frames -> tasks//images
  2. 预标注（可选）：auto_label_yolo.py -> prelabel/yolo
  3. 人工标注：输出 tasks//labels
  4. 导出：复制 tasks/ 到 exports/
  5. 训练集仓库同步：exports/ -> train dataset

版本挂钩

  • 标注仓库版本：annot_v001（tag）
  • 训练集仓库引用：dataset_meta.json 中记录 source_annotation_revision
  • 导出版本：exports//export_meta.json

注意事项

  • images 与 labels 必须同名一一对应
  • 只在 exports/ 下形成“可训练的干净版本”
  • curated/ 仅用于分析与回溯，不进入训练集
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