bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes

--- configs: - config_name: default data_files: - split: train path: json_data/STGR-SFT-motion-mixed-PLM-motion-only.json --- # Motion-o-MCoT (PLM + motion keyframes) Subset of STGR: `STR_plm_rdcap` rows with `<motion` in `reasoning_process`, plus sharded keyframes under `videos/stgr/plm/kfs/`. - **Train split:** 3,168 examples (see `export_manifest.json` in the repo for exact export stats). - **Keyframes:** JPEGs are stored under shard subfolders (e.g. `videos/stgr/plm/kfs/plm_0150/…`) so each directory stays under Hugging Face file-count limits. Each `key_frames[].path` in the JSON is **relative to** `videos/stgr/plm/kfs/` (e.g. `plm_0150/plm_015078_0_time_0.5.jpg`). - **Videos:** Full PLM source videos are **not** included in this release; `video_path_full` in the JSON is a **relative** path under the dataset root (e.g. `videos/sav_036475.mp4`) for compatibility with a full STGR tree if you have it locally. ## Download ### Hugging Face CLI (`hf`) ```bash hf auth login # Full snapshot (JSON + all keyframe images) hf download bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes \ --repo-type dataset \ --local-dir ./Motion-o-MCoT-PLM-motion-keyframes ``` ### Python (`huggingface_hub`) ```python from huggingface_hub import snapshot_download snapshot_download( repo_id="bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes", repo_type="dataset", local_dir="./Motion-o-MCoT-PLM-motion-keyframes", ) ``` ### JSON only (small) ```bash hf download bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes \ --repo-type dataset \ --include "json_data/*" \ --include "export_manifest.json" \ --local-dir ./Motion-o-MCoT-PLM-motion-keyframes ``` ## Use with `datasets` ```python from datasets import load_dataset ds = load_dataset( "bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes", split="train", ) print(len(ds), ds[0].keys()) print(ds[0]["key_frames"][0]["path"]) # e.g. plm_0150/plm_015078_0_time_0.5.jpg ``` ## Resolve keyframe files on disk After downloading, set a root directory `DATA_ROOT` to the folder that contains both `json_data/` and `videos/`. Then each keyframe absolute path is: ```text {DATA_ROOT}/videos/stgr/plm/kfs/{key_frames[i]["path"]} ``` Example: ```python import os DATA_ROOT = "./Motion-o-MCoT-PLM-motion-keyframes" # or absolute path rel = ds[0]["key_frames"][0]["path"] abs_path = os.path.join(DATA_ROOT, "videos", "stgr", "plm", "kfs", rel) assert os.path.isfile(abs_path), abs_path ``` This matches the layout expected by the Motion-o / Open-o3 training code when `DATA_ROOT` points at a full STGR-style tree (`videos/stgr/plm/kfs/` + basename or shard-relative path in JSON). ## Hub URL [https://huggingface.co/datasets/bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes](https://huggingface.co/datasets/bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes) ## Citation If you use this subset, please cite the Motion-o paper and the STGR / Open-o3 Video sources as appropriate. See the main project repository for BibTeX.--- 配置项： - 配置名称：default 数据文件： - 拆分集：训练集（train） 文件路径：json_data/STGR-SFT-motion-mixed-PLM-motion-only.json --- # Motion-o-MCoT（预训练语言模型（Pre-trained Language Model，PLM）+ 运动关键帧） 本数据集是STGR的子集：选取`STR_plm_rdcap`中`reasoning_process`字段包含`<motion`的样本，并附带`videos/stgr/plm/kfs/`路径下的分片式关键帧数据。 - **训练拆分集：** 共3168条样本，完整的导出统计信息请查看仓库中的`export_manifest.json`文件。 - **关键帧：** JPEG格式图像存储于分片子文件夹中（例如`videos/stgr/plm/kfs/plm_0150/…`），以此确保每个目录的文件数量符合Hugging Face的平台限制要求。JSON文件中的每个`key_frames[].path`均为相对于`videos/stgr/plm/kfs/`的相对路径（例如`plm_0150/plm_015078_0_time_0.5.jpg`）。 - **视频文件：** 本版本未包含完整的PLM源视频；JSON文件中的`video_path_full`为数据集根目录下的相对路径（例如`videos/sav_036475.mp4`），方便你在本地拥有完整STGR数据集时兼容使用。 ## 下载方式 ### Hugging Face 命令行工具（`hf`） bash hf auth login # 完整快照（包含JSON文件与所有关键帧图像） hf download bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes --repo-type dataset --local-dir ./Motion-o-MCoT-PLM-motion-keyframes ### Python 环境（`huggingface_hub`库） python from huggingface_hub import snapshot_download snapshot_download( repo_id="bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes", repo_type="dataset", local_dir="./Motion-o-MCoT-PLM-motion-keyframes", ) ### 仅下载JSON文件（体积小巧） bash hf download bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes --repo-type dataset --include "json_data/*" --include "export_manifest.json" --local-dir ./Motion-o-MCoT-PLM-motion-keyframes ## 结合`datasets`库使用 python from datasets import load_dataset ds = load_dataset( "bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes", split="train", ) print(len(ds), ds[0].keys()) print(ds[0]["key_frames"][0]["path"]) # 示例：plm_0150/plm_015078_0_time_0.5.jpg ## 本地磁盘关键帧文件路径解析 下载完成后，请将包含`json_data/`与`videos/`的文件夹路径设为根目录`DATA_ROOT`。此时单条关键帧的绝对路径格式为： text {DATA_ROOT}/videos/stgr/plm/kfs/{key_frames[i]["path"]} 示例： python import os DATA_ROOT = "./Motion-o-MCoT-PLM-motion-keyframes" # 或使用绝对路径 rel = ds[0]["key_frames"][0]["path"] abs_path = os.path.join(DATA_ROOT, "videos", "stgr", "plm", "kfs", rel) assert os.path.isfile(abs_path), abs_path 当`DATA_ROOT`指向完整的STGR格式数据集目录（即`videos/stgr/plm/kfs/` + JSON中存储的basename或分片相对路径）时，该路径格式可匹配Motion-o / Open-o3训练代码所需的目录结构。 ## 数据集仓库链接 [https://huggingface.co/datasets/bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes](https://huggingface.co/datasets/bishoygaloaa/Motion-o-MCoT-PLM-motion-keyframes) ## 引用声明 若你使用该子集，请酌情引用Motion-o相关论文以及STGR/Open-o3视频数据集来源。BibTeX引用格式请查阅主项目仓库。