You'll need to provide continuous, non-overlapping labels from 0 to ms, with startMs and endMs given as a multiple of the sample interval (ms).
Timestamps are inclusive - so {"startMs": 200, "endMs": 202 } includes 200, 201 and 202.
Edit labels for '''
You'll need to provide string key-value pairs for each label in the sample, for example: { "color": "blue", "type": "car" }.
To remove a label, remove the key and value from the label map.
You can upload CBOR, JSON, CSV, Parquet, WAV, JPG, PNG, AVI or MP4 files. You can also upload an annotation file named "info.labels"
with your data to assign bounding boxes, labels, and/or metadata.
View Uploader docs to learn more.
Alternatively, you can use our
Python SDK
to programmatically ingest data in various formats, such as pandas or numpy;
or import data directly from another project.
CSV and Parquet files will be processed using the active CSV Wizard configuration, which was created using
"fireUP.csv"
().
Download config.
Bounding boxes: You can upload object detection datasets in
any supported format.
Select both your images and the label file(s) when uploading to apply the labels.
Upload mode
Select files
Using the info.labels file for labels and category.
Image label format
Labeling method
Annotations in this format could not be found in the selected files.
Select both your images and any label files when uploading to apply the labels.
A label map file could not be detected.
This format requires a label map file, which maps keys to the label they represent.
You can fix these labels later by clicking 'Edit labels' on the data acquisition page.
Upload into category
Upload category will be derived from the structure of your dataset
(e.g. samples in a 'train' directory will be uploaded into training data).
Label
CSV and Parquet files will be labeled using the 'Fire Alarm' column, as configured in the
CSV wizard. Update configuration .
You need to specify a label
Advanced settings
This dataset format uses bounding box labeling, used for object detection.
This dataset format uses one label per sample. You may wish to change your project
labeling method to 'one label per data item' in the project dashboard.
Use this modal to configure how your dataset is split
between training and testing,
while optionally preserving label or metadata distribution through stratification.
Choose your split and stratification options, click Preview to inspect the outcome, then click Split dataset to apply it.
The selected samples contain the following labels. Which ones do you want to edit?
Set the label (leave empty to remove these labels):
Dataset train / test split ratio
Training data is used to train your model, and testing data is used to test your model's accuracy after training.
We recommend an approximate 80/20 train/test split ratio for your data for every class (or label) in your dataset, although especially large datasets may require less testing data.
Suggested train / test split
80% / 20%
Labels in your dataset
Perform train / test split
Use this option to rebalance your data, automatically splitting items between training and testing datasets. Warning: this action cannot be undone.
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Configure your target device and application budget
Target device
Define your target device requirements to inform model optimizations and performance calculations.
No device yet? Use the default settings which you can change at any time.
| MHz
Max
Application budget
Specify the available RAM and ROM for the model's operation, along with the maximum allowed latency
for your specific application. Not sure yet? Start with the defaults and modify them later on.
| KB
Max
| KB
Max
| ms
Max
Choose your pricing
YEARLY
$400
/month
billed annually
SAVE 15%
MONTHLY
$475
/month
billed monthly
Additional usage
Professional Plan includes 1,000 compute minutes per month. Additional usage will be charged at $0.10 per minute, billed monthly.
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