The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
Error code: DatasetGenerationError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 281, in _generate_tables
examples = [ujson_loads(line) for line in batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
messages list | uid string | task string | dataset string | duration_s float64 |
|---|---|---|---|---|
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"text": "You are the verifier in a multi-angle video reasoning sys... | acdff5d615ab8397 | verifier | CG-Bench | 2,466.033 |
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
RunningBench 人工标注 · 标注说明
本仓库是 RunningBench 人工标注的分发包。10 个包 annotation_bundles/bundle_01.tar … bundle_10.tar,共 1526 题,每包约 150 题,
按题源分层(fullvideo / excerpt / p01ma_gdrive / p01ma_hf / rbma273)。包内不含标准答案。
English version → README_EN.md
1. 你要做什么(操作流程)
任务:每题给出若干视频片段和 6–8 个选项,题目要求选恰好 n 项(多为 3 项)。你需要看视频后判断每一个选项是否被画面支持, 给出最终答案,并判断这道题本身是否清晰。不做盲答,直接看视频。
准备:下载你分到的那个 tar,解压。不需要安装任何软件,不需要联网。
步骤
- 双击
review.html,右上角填写你的姓名(导出文件会带上)。 - 每题四步:
- ① 看:把该题的全部片段看完,边看边在纸上或文本里记一条时间线:转向(方向、大概角度)、路面变化、带数字/文字/颜色的物体、 行人车辆、显著建筑。时间不用精确,±3 秒即可,要的是先后顺序和大致位置。多片段题每个片段单独记一条, 注意 CLIP 标签顺序不代表时间顺序(CLIP_A 不一定比 CLIP_B 早)。
- ② 逐选项核对:对每个选项点一个标记,不许凭整体印象:
- ✓ 支持:画面里找得到支撑它的那几秒。
- ✗ 排除:画面明确否定它。一处细节错即为错(左右反了、顺序反了、颜色不对)。画面覆盖了该地点/时刻但没有该物体,也算排除。
- ? 无法判定:画面根本没拍到该选项说的地点/时刻。
- ③ 最终答案:勾恰好 n 项,默认跟随 ✓。✓ 数量 ≠ n 时不要硬凑:填你最有把握的 n 项,判定选 ambiguous,备注写清是哪个选项导致的。
- ④ 判定 + 备注:
clear:只有一组说得通的答案,且画面看得见。ambiguous:存在第二组说得通的答案,或题干有歧义,或 ✓ 数量与 n 不符。cant_tell:画面模糊、缺失,或没覆盖到关键时刻。- 备注写:哪个选项有问题、你看到了什么、大约第几秒。这是最有价值的反馈。
- 做完一题卡片变绿;页面顶部可筛选"未完成"。进度自动保存在本机浏览器,随时关闭、下次接着做。 换电脑:先点「导出 JSON」,在新电脑上用「导入进度」恢复。
- 全部完成后点右上「导出 JSON」,把下载到的
annotation_<姓名>_<日期>.json回传。
工作量:单片段题 2–3 分钟,多片段比较题 4–6 分钟,一天约 80–100 题。每 90 分钟休息一次,疲劳会明显降低对近似干扰项的识别率。
片段命名:CLIP_1, CLIP_2… 是同一段录像按时间顺序切出的多段;CLIP_A…CLIP_J 是题干里点名的匿名片段,顺序与时间无关;
VIDEO_A…VIDEO_R 是跨录像比较题里题干用到的匿名录像编号。
2. 怎么看、怎么判(校验手册全文)
本流程不做盲答,下文凡提到"盲答 / Step 1"的句子请忽略,其余全部适用。
0. 一条总原则
逐选项对账,不做整体印象判断。
错误做法:看完视频,凭整体感觉选出"对的三个"。 正确做法:8 个选项一个一个过,每个选项在视频里找到"支持它的画面"或 "否定它的画面",找不到就标记为"判不了"。
为什么:这批题的干扰项是故意造的 near-miss —— 拿画面里真实存在的东西, 改掉一两处细节(左右对调、顺序颠倒、颜色写错)。凭印象看,near-miss 全都"眼熟", 你会漏掉被改的那一两处。只有逐项对账能抓出来。
1. 两遍看法
第一遍 · 连续看完,记时间线
不看选项(或看过就先放下),从头到尾连续播放,手上记一条时间线:
0:05 出发,柏油路,路灯亮着
0:18 右转,进入鹅卵石路面
0:31 左侧出现编号 11 的建筑
0:44 对面骑车人(车前灯亮)驶过
0:58 经过自行车停放架
1:12 两根红白条纹护柱之间穿过
1:27 右侧混凝土长椅
1:40 红白施工围挡开始
记什么:转弯(方向+大概角度)、路面变化、有编号/文字/颜色的物体、 移动的人和车、明显的建筑。不用记全,记"可能被出题的东西"。
时间戳不用精确,±3 秒足够 —— 你要的是顺序和大概位置,不是秒表。
多片段的题(题干里有 CLIP_B、CLIP_F 这类标签):每个片段各记一条时间线, 标签写在开头。注意:标签顺序不代表时间顺序,CLIP_A 未必比 CLIP_B 早。
第二遍 · 逐选项定点核对
现在打开选项,一个一个过。每个选项:
- 拆出它声称的事实。比如选项 B: "穿过两根条纹护柱进入有混凝土长椅的路段,早于经过红白施工围挡" 拆成三件事:① 有条纹护柱 ② 护柱后有长椅 ③ 这些都在施工围挡之前
- 对照你的时间线。护柱 1:12、长椅 1:27、围挡 1:40 —— 三件事都对 → 支持
- 拿不准就拖回去看。用播放器拖到时间线上对应位置,只看那几秒。 这就是记时间线的意义:第二遍不用重看全片,只做定点核查。
每个选项在纸上标一个符号:✓ 支持 · ✗ 否定(写明哪一处错)· ? 判不了
全部 8 个过完再下结论。✓ 应该恰好 3 个 —— 多了少了都说明有问题 (要么标注错,要么题目歧义),这本身就是你要报告的发现。
2. 每类题看什么
| 题型 | 第一遍重点记 | 第二遍核对什么 |
|---|---|---|
event-order 事件顺序 |
每个显著事件的时间戳 | 选项里"X 早于 Y"逐对核对时间线 |
turn-pattern 转弯模式 |
每次转弯:方向、急缓、路口特征 | 选项说的方向/角度/次序有没有写反 |
landmark-order 地标顺序 |
地标出现时刻 + 在路的哪一侧 | 顺序 + 左右侧(最常被改的细节) |
landmark-revisit 重复经过 |
同一物第二次出现的时刻和方向 | 是不是真的同一个物(对细节:颜色/文字/损伤) |
environment-* 环境变化 |
路面材质/植被/建筑密度的切换点 | 变化方向对不对("从柏油到碎石"还是反过来) |
spatial-consistency 空间关系 |
关键物在路的左/右/前方 | 去程右侧 = 回程左侧,别被折返骗了 |
same-place-different-recording |
两个片段各自的地标清单 | 逐个地标比对:同一颗树、同一块牌子? |
route-identity 是否同一条路 |
各片段的路面、转弯序列 | 转弯序列一致才算同路,单个地标相似不够 |
跨录像的题(对比两次不同录制)额外注意:
- 光照可以完全不同(一次白天一次夜里),别因为"看起来不像"就判不同地点
- 判"同一地点"要靠不变的东西:建筑形状、路的走向、固定设施
- 判"不同"要靠结构差异:转弯序列不同、路面材质不同,而不是行人车辆这些偶然物
3. 判定规则(容易搞错的三种情况)
① 选项说的东西画面里根本没有
看情况,这是最容易判错的一类:
- 片段完整覆盖了选项声称的位置/时段,那里没这东西 → ✗ 否定。 例:选项说"深灰色旅行车停在长椅旁",片段清楚拍到了长椅一带,没有任何车 → 否定。
- 片段没拍到选项说的位置/时段 → ? 判不了。 例:选项讲"过桥之后…"而片段在过桥前就结束了 → 判不了。
区别在于:画面给没给你否定它的机会。
② 选项大部分对、一处错
→ ✗ 否定。 一处错就是错。near-miss 干扰项就长这样, "长椅在右侧"(实际在左侧)不能因为"确实有长椅"就放过。
③ 你看到的和标注答案数对不上
数出 4 个 ✓ 或只有 2 个 ✓ 时,不要硬凑成 3 个。如实选:
- 你的三个最有把握的作为"最终答案"
- 判定选
ambiguous(多于 3 个说得通)或wrong_answer - 备注里写清是哪个选项出的问题 —— 这是最有价值的反馈
4. 完整示范(真题)
题型 event-order · 2 个片段(CLIP_C、CLIP_F,同一夜跑录像的两段)
Q: 以下哪些选项正确描述了路线上事件的时间顺序?选三个。
A. 在到达标有数字 11 的建筑之前,先与亮着车前灯的迎面骑车人擦肩而过 B. 穿过两根条纹护柱进入有混凝土长椅的路段,早于经过红白施工围挡 C. 经过红白施工围挡早于穿过两根条纹护柱 ← 注意:恰好是 B 的反命题 D. 在沿着经过施工围挡的直路之后,才在路右缘遇到混凝土长椅 E. 在鹅卵石路段经过 11 号建筑,早于到达室外自行车架 F. 迎面骑车人是在有施工围挡的直路柏油段遇到的,而非弯曲鹅卵石路段 G. 先经过 11 号建筑,之后才在鹅卵石路上遇到迎面骑车人 ← A 的反命题 H. 在鹅卵石路上经过自行车架,早于走上路灯下草坪相夹的直路
第一遍,两个片段各记时间线:
CLIP_C: 0:03 鹅卵石路 · 0:15 迎面骑车人(车灯) · 0:29 建筑"11" · 0:41 自行车架
CLIP_F: 0:05 条纹护柱×2 · 0:12 混凝土长椅(右) · 0:limit 草坪直路(路灯) · 0:47 施工围挡
还差一件事:CLIP_C 和 CLIP_F 谁先谁后? 标签不代表顺序。 线索:CLIP_H 段选项说"鹅卵石路早于草坪直路" —— 你得从画面推 (比如 CLIP_C 结束处的场景是否衔接 CLIP_F 开头,或看两段的疲劳程度/环境延续性), 推不出就依靠单段内部能判定的选项。
第二遍逐项:
A 骑车人(C 0:15) 早于建筑11(C 0:29) → ✓ 同段内可判
B 护柱(F 0:05) 早于围挡(F 0:47) → ✓ 同段内可判
C 与 B 相反 → ✗
D 长椅(F 0:12) 在围挡(F 0:47) 之前,选项说在后 → ✗
E 建筑11(0:29) 早于车架(0:41)?选项方向对 → 等等,再核一遍…✓? 但标注答案没有 E!
→ 重看 0:29-0:41:车架其实 0:26 就在画面边缘出现过 → E 实际为 ✗
F 骑车人在鹅卵石段(C),选项说在柏油围挡段 → ✗
G 与 A 相反 → ✗
H 车架(C 0:41,鹅卵石) 早于草坪直路(F) → ✓ 需要跨段顺序,由画面衔接判定
✓ = A、B、H,恰好 3 个,与标注一致 → 判定 correct。
注意 E 那一步:差点被自己的时间线骗了 —— 第一遍漏记了 0:26 画面边缘的车架。 所以拿不准的选项一定要拖回去重看,时间线是索引,不是证据。
5. 常见错误清单
| 错误 | 后果 |
|---|---|
| 凭整体印象选三个,不逐项对账 | near-miss 干扰项全漏 |
| 先看视频再补盲猜 | 盲猜数据作废,可猜率虚低 |
| "画面里没有"一律判否定 | 把"判不了"误当"否定",高估标注质量 |
| 折返路段忘了左右互换 | spatial 类题全判反 |
| 把 CLIP 标签顺序当时间顺序 | event-order 题判反 |
| 凑答案数(明明看到 4 个对,硬选 3 个) | 掩盖了题目歧义,这正是要报告的 |
| 跨录像题因光照不同判"不同地点" | 昼夜对比题全错 |
6. 一天能校多少
熟练后单片段题约 2-3 分钟/道,多片段对比题 4-6 分钟/道。 一天 6 小时约 80-100 道。 抽样 120 道(推荐)约一天半。
疲劳会显著降低 near-miss 的辨识率 —— 连续校验不要超过 90 分钟,休息再来。
3. 回传与验收
- 回传:每人一个导出的 JSON 文件,不需要改任何内容。
- 验收线:标注一致率 > 90%(由回收方用标准答案合并计算)。一致率低不是你的问题,是题的问题,如实报告最重要。
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