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Dataset v3 (#1412)

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Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Co-authored-by: Remi Cadene <re.cadene@gmail.com>
Co-authored-by: Tavish <tavish9.chen@gmail.com>
Co-authored-by: fracapuano <francesco.capuano@huggingface.co>
Co-authored-by: CarolinePascal <caroline8.pascal@gmail.com>
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Michel Aractingi
2025-09-15 09:53:30 +02:00
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README.md
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@@ -233,7 +233,7 @@ Under the hood, the `LeRobotDataset` format makes use of several ways to seriali
Here are the important details and internal structure organization of a typical `LeRobotDataset` instantiated with `dataset = LeRobotDataset("lerobot/aloha_static_coffee")`. The exact features will change from dataset to dataset but not the main aspects:
```
````
dataset attributes:
├ hf_dataset: a Hugging Face dataset (backed by Arrow/parquet). Typical features example:
│ ├ observation.images.cam_high (VideoFrame):
@@ -246,20 +246,30 @@ dataset attributes:
│ ├ timestamp (float32): timestamp in the episode
│ ├ next.done (bool): indicates the end of an episode ; True for the last frame in each episode
│ └ index (int64): general index in the whole dataset
├ episode_data_index: contains 2 tensors with the start and end indices of each episode
│ ├ from (1D int64 tensor): first frame index for each episode — shape (num episodes,) starts with 0
│ └ to: (1D int64 tensor): last frame index for each episode — shape (num episodes,)
├ stats: a dictionary of statistics (max, mean, min, std) for each feature in the dataset, for instance
│ ├ observation.images.cam_high: {'max': tensor with same number of dimensions (e.g. `(c, 1, 1)` for images, `(c,)` for states), etc.}
│ ...
├ info: a dictionary of metadata on the dataset
│ ├ codebase_version (str): this is to keep track of the codebase version the dataset was created with
│ ├ fps (float): frame per second the dataset is recorded/synchronized to
│ ├ video (bool): indicates if frames are encoded in mp4 video files to save space or stored as png files
│ └ encoding (dict): if video, this documents the main options that were used with ffmpeg to encode the videos
├ videos_dir (Path): where the mp4 videos or png images are stored/accessed
└ camera_keys (list of string): the keys to access camera features in the item returned by the dataset (e.g. `["observation.images.cam_high", ...]`)
```
├ meta: a LeRobotDatasetMetadata object containing:
│ ├ info: a dictionary of metadata on the dataset
│ │ ├ codebase_version (str): this is to keep track of the codebase version the dataset was created with
│ │ ├ fps (int): frame per second the dataset is recorded/synchronized to
│ │ ├ features (dict): all features contained in the dataset with their shapes and types
│ │ ├ total_episodes (int): total number of episodes in the dataset
│ │ ├ total_frames (int): total number of frames in the dataset
│ │ ├ robot_type (str): robot type used for recording
│ │ ├ data_path (str): formattable string for the parquet files
│ │ └ video_path (str): formattable string for the video files (if using videos)
│ ├ episodes: a DataFrame containing episode metadata with columns:
│ │ ├ episode_index (int): index of the episode
│ │ ├ tasks (list): list of tasks for this episode
│ │ ├ length (int): number of frames in this episode
│ │ ├ dataset_from_index (int): start index of this episode in the dataset
│ │ └ dataset_to_index (int): end index of this episode in the dataset
│ ├ stats: a dictionary of statistics (max, mean, min, std) for each feature in the dataset, for instance
│ │ ├ observation.images.front_cam: {'max': tensor with same number of dimensions (e.g. `(c, 1, 1)` for images, `(c,)` for states), etc.}
│ │ └ ...
│ └ tasks: a DataFrame containing task information with task names as index and task_index as values
├ root (Path): local directory where the dataset is stored
├ image_transforms (Callable): optional image transformations to apply to visual modalities
└ delta_timestamps (dict): optional delta timestamps for temporal queries
decoding videos (e.g., 'pyav', 'torchcodec')
A `LeRobotDataset` is serialised using several widespread file formats for each of its parts, namely:
@@ -283,7 +293,7 @@ lerobot-eval \
--eval.n_episodes=10 \
--policy.use_amp=false \
--policy.device=cuda
```
````
Note: After training your own policy, you can re-evaluate the checkpoints with: