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Nekhil R
/
Fire Detection with sensor fusion
Public
Crop sample ''
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You'll need to provide continuous, non-overlapping labels from 0 to ms.
Timestamps are inclusive - so
{"startMs": 200, "endMs": 202 } includes 200, 201 and 202.
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Upload data
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.
For CSV and Parquet files, configure the CSV Wizard to define how your files should be processed before uploading files.
For CSV and Parquet files, configure the CSV Wizard to define how your files should be processed before uploading files.
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
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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.
Upload output
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Use the 'Record new data' section to collect data from this device.
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Edit labels
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0%
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
Fire
83% / 17% (5m 0s / 1m 0s)
No fire
83% / 17% (5m 0s / 1m 0s)
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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