An accesskey is an access credential for identification when using TensorBay to operate on your dataset.
The basehead is the string for recording the two relative versions(commits or drafts) in the format of “base…head”.
The basehead param is comprised of two parts: base and head. Both must be revision or draft number in dataset. The terms “head” and “base” are used as they normally are in Git.
The head is the version which changes are on. The base is the version of which these changes are based.
Similar to git, a branch is a lightweight pointer to one of the commits.
Every time a commit is submitted, the main branch pointer moves forward automatically to the latest commit.
Similar with Git, a commit is a version of a dataset, which contains the changes compared with the former commit.
Each commit has a unique commit ID, which is a uuid in a 36-byte hexadecimal string. A certain commit of a dataset can be accessed by passing the corresponding commit ID or other forms of revision.
A commit is readable, but is not writable. Thus, only read operations such as getting catalog, files and labels are allowed. To change a dataset, please create a new commit. See draft for details.
On the other hand, “commit” also represents the action to save the changes inside a draft into a commit.
A dataset is continuous means the data in each segment of the dataset is collected over a continuous period of time and the collection order is indicated by the data paths or frame indexes.
The continuity can be set in notes.
Only continuous datasets can have tracking labels.
Here are some dataloader examples of datasets with different label types and continuity(Table. 5).
The name of the dataloader function is a unique indentification of the dataset. It is in upper camel case and is generally obtained by removing special characters from the dataset name.
See more dataloader examples in tensorbay.opendataset.
A uniform dataset format defined by TensorBay, which only contains one type of data collected from one sensor or without sensor information. According to the time continuity of data inside the dataset, a dataset can be a discontinuous dataset or a continuous dataset. Notes can be used to specify whether a dataset is continuous.
The corresponding class of dataset is
See Dataset Structure for more details.
TensorBay supports showing the status difference of the relative resource between commits or drafts in the form of diff.
Similar with Git, a draft is a workspace in which changing the dataset is allowed.
A draft is created based on a branch, and the changes inside it will be made into a commit.
There are scenarios when modifications of a dataset are required, such as correcting errors, enlarging dataset, adding more types of labels, etc. Under these circumstances, create a draft, edit the dataset and commit the draft.
A uniform dataset format defined by Tensorbay, which contains data collected from multiple sensors.
According to the time continuity of data inside the dataset, a fusion dataset can be a discontinuous fusion dataset or a continuous fusion dataset. Notes can be used to specify whether a fusion dataset is continuous.
The corresponding class of fusion dataset is
See Fusion Dataset Structure for more details.
Similar to Git, a revision is a reference to a single commit. And many methods in TensorBay SDK take revision as an argument.
Currently, a revision can be in the following forms:
TensorBay SDK has the ability to tag the specific commit in a dataset’s history as being important. Typically, people use this functionality to mark release points (v1.0, v2.0 and so on).
TBRN is the abbreviation for TensorBay Resource Name, which represents the data or a collection of data stored in TensorBay uniquely.
Note that TBRN is only used in CLI.
TBRN begins with
tb:, followed by the dataset name, the segment name and the file name.
The following is the general format for TBRN:
Suppose there is an image
000000.jpg under the
train segment of a dataset named
then the TBRN of this image should be:
The labels of a dataset are tracking means the labels contain tracking information, such as tracking ID, which is used for tracking tasks.