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Test out a trained impulse (using a posted image).
Project ID
Impulse ID. If this is unset then the default impulse is used.
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Classification value per label. For a neural network this will be the confidence, for anomalies the anomaly score.
This API is deprecated, use classifySampleV2 instead (/v1/api/{projectId}/classify/v2/{sampleId}). Classify a complete file against the current impulse. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted.
Project ID
Sample ID
Whether to return the debug information from FOMO classification.
Impulse ID. If this is unset then the default impulse is used.
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Identifier for this block. Make sure to up this number when creating a new block, and don't re-use identifiers. If the block hasn't changed, keep the ID as-is. ID must be unique across the project and greather than zero (>0).
The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression). Each behaves differently.
Block name, will be used in menus. If a block has a baseBlockId, this field is ignored and the base block's name is used instead.
"NN Classifier"DSP dependencies, identified by DSP block ID
27Block title, used in the impulse UI
"Classification (Keras)"A short description of the block version, displayed in the block versioning UI
"Reduced learning rate and more layers"The system component that created the block version (createImpulse | clone | tuner). Cannot be set via API.
"createImpulse"The datetime that the block version was created. Cannot be set via API.
Classification result, one item per window.
Anomaly scores and computed metrics for visual anomaly detection, one item per window.
For visual anomaly detection. An array of bounding box objects, (x, y, width, height, score, label), one per detection in the image. Filtered by the minimum confidence rating of the learn block.
2D array of shape (n, n) with raw anomaly scores for visual anomaly detection, where n can be calculated as ((1/8 of image input size)/2 - 1). The scores corresponds to each grid cell in the image's spatial matrix.
Mean value of the scores.
Maximum value of the scores.
Results of inferencing that returns structured data, such as object detection
For object detection. An array of bounding box arrays, (x, y, width, height), one per detection in the image.
For object detection. An array of labels, one per detection in the image.
For object detection. An array of probability scores, one per detection in the image.
For object detection. A score that indicates accuracy compared to the ground truth, if available.
For FOMO. A score that combines the precision and recall of a classifier into a single metric, if available.
A measure of how many of the positive predictions made are correct (true positives).
A measure of how many of the positive cases the classifier correctly predicted, over all the positive cases.
Debug info in JSON format
"{\n \"y_trues\": [\n {\"x\": 0.854, \"y\": 0.453125, \"label\": 1},\n {\"x\": 0.197, \"y\": 0.53125, \"label\": 2}\n ],\n \"y_preds\": [\n {\"x\": 0.916, \"y\": 0.875, \"label\": 1},\n {\"x\": 0.25, \"y\": 0.541, \"label\": 2}\n ],\n \"assignments\": [\n {\"yp\": 1, \"yt\": 1, \"label\": 2, \"distance\": 0.053}\n ],\n \"normalised_min_distance\": 0.2,\n \"all_pairwise_distances\": [\n [0, 0, 0.426],\n [1, 1, 0.053]\n ],\n \"unassigned_y_true_idxs\": [0],\n \"unassigned_y_pred_idxs\": [0]\n}\n"The minimum confidence rating for this block. For regression, this is the absolute error (which can be larger than 1).
Structured outputs and computed metrics for some model types (e.g. object detection), one item per window.
Bounding boxes predicted by localization model
Labels predicted by localization model
Scores predicted by localization model
For object detection, the COCO mAP computed for the predictions on this image
For FOMO, the F1 score computed for the predictions on this image
An array with an expected label per window.
Start index of the label (e.g. 0)
End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
The label for this section.
2"idle01.d8Ae"Whether signature validation passed
true"HS256"Either the shared key or the public key that was used to validate the sample
Timestamp when the sample was created on device, or if no accurate time was known on device, the time that the file was processed by the ingestion service.
Timestamp when the sample was last modified.
"training""healthy-machine"Interval between two windows (1000 / frequency). If the data was resampled, then this lists the resampled interval.
16Frequency of the sample. If the data was resampled, then this lists the resampled frequency.
62.5Interval between two windows (1000 / frequency) in the source data (before resampling).
16Frequency of the sample in the source data (before resampling).
62.5Name of the axis
"accX"Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml).
Number of readings in this file
Total length (in ms.) of this file
Timestamp when the sample was added to the current acquisition bucket.
True if the current sample is excluded from use
True if the current sample is still processing (e.g. for video)
Set when sample is processing and a job has picked up the request
Set when processing this sample failed
Error (only set when processing this sample failed)
Whether the sample is cropped from another sample (and has crop start / end info)
Sample free form associated metadata
Unique identifier of the project this sample belongs to
Name of the owner of the project this sample belongs to
Name of the project this sample belongs to
What labeling flow the project this sample belongs to uses
Data sample SHA 256 hash (including CBOR envelope if applicable)
Start index of the label (e.g. 0)
End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
The label for this section.
If this sample was created by a synthetic data job, it's referenced here.
Sensor readings and metadata
Unique identifier for this device. Only set this when the device has a globally unique identifier (e.g. MAC address).
"ac:87:a3:0a:2d:1b"Device type, for example the exact model of the device. Should be the same for all similar devices.
"DISCO-L475VG-IOT01A"Array with sensor axes
Name of the axis
"accX"Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml).
Array of sensor values. One array item per interval, and as many items in this array as there are sensor axes. This type is returned if there are multiple axes.
New start index of the cropped sample
0New end index of the cropped sample
128Total number of payload values
Size of the sliding window (as set by the impulse) in milliseconds.
2996Number of milliseconds that the sliding window increased with (as set by the impulse)
10Whether this sample is already in the training database
Get classify job result, containing the predictions for a given page.
Project ID
Maximum number of results
Offset in results, can be used in conjunction with LimitResultsParameter to implement paging.
Keras model variant
Impulse ID. If this is unset then the default impulse is used.
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
2"idle01.d8Ae"Whether signature validation passed
true"HS256"Either the shared key or the public key that was used to validate the sample
Timestamp when the sample was created on device, or if no accurate time was known on device, the time that the file was processed by the ingestion service.
Timestamp when the sample was last modified.
"training""healthy-machine"Interval between two windows (1000 / frequency). If the data was resampled, then this lists the resampled interval.
16Frequency of the sample. If the data was resampled, then this lists the resampled frequency.
62.5Interval between two windows (1000 / frequency) in the source data (before resampling).
16Frequency of the sample in the source data (before resampling).
62.5Name of the axis
"accX"Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml).
Number of readings in this file
Total length (in ms.) of this file
Timestamp when the sample was added to the current acquisition bucket.
True if the current sample is excluded from use
True if the current sample is still processing (e.g. for video)
Set when sample is processing and a job has picked up the request
Set when processing this sample failed
Error (only set when processing this sample failed)
Whether the sample is cropped from another sample (and has crop start / end info)
Sample free form associated metadata
Unique identifier of the project this sample belongs to
Name of the owner of the project this sample belongs to
Name of the project this sample belongs to
What labeling flow the project this sample belongs to uses
Data sample SHA 256 hash (including CBOR envelope if applicable)
Start index of the label (e.g. 0)
End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
The label for this section.
If this sample was created by a synthetic data job, it's referenced here.
Identifier for this block. Make sure to up this number when creating a new block, and don't re-use identifiers. If the block hasn't changed, keep the ID as-is. ID must be unique across the project and greather than zero (>0).
The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression). Each behaves differently.
Block name, will be used in menus. If a block has a baseBlockId, this field is ignored and the base block's name is used instead.
"NN Classifier"DSP dependencies, identified by DSP block ID
27Block title, used in the impulse UI
"Classification (Keras)"A short description of the block version, displayed in the block versioning UI
"Reduced learning rate and more layers"The system component that created the block version (createImpulse | clone | tuner). Cannot be set via API.
"createImpulse"The datetime that the block version was created. Cannot be set via API.
Classification result, one item per window.
Anomaly scores and computed metrics for visual anomaly detection, one item per window.
For visual anomaly detection. An array of bounding box objects, (x, y, width, height, score, label), one per detection in the image. Filtered by the minimum confidence rating of the learn block.
2D array of shape (n, n) with raw anomaly scores for visual anomaly detection, where n can be calculated as ((1/8 of image input size)/2 - 1). The scores corresponds to each grid cell in the image's spatial matrix.
Mean value of the scores.
Maximum value of the scores.
Results of inferencing that returns structured data, such as object detection
For object detection. An array of bounding box arrays, (x, y, width, height), one per detection in the image.
For object detection. An array of labels, one per detection in the image.
For object detection. An array of probability scores, one per detection in the image.
For object detection. A score that indicates accuracy compared to the ground truth, if available.
For FOMO. A score that combines the precision and recall of a classifier into a single metric, if available.
A measure of how many of the positive predictions made are correct (true positives).
A measure of how many of the positive cases the classifier correctly predicted, over all the positive cases.
Debug info in JSON format
"{\n \"y_trues\": [\n {\"x\": 0.854, \"y\": 0.453125, \"label\": 1},\n {\"x\": 0.197, \"y\": 0.53125, \"label\": 2}\n ],\n \"y_preds\": [\n {\"x\": 0.916, \"y\": 0.875, \"label\": 1},\n {\"x\": 0.25, \"y\": 0.541, \"label\": 2}\n ],\n \"assignments\": [\n {\"yp\": 1, \"yt\": 1, \"label\": 2, \"distance\": 0.053}\n ],\n \"normalised_min_distance\": 0.2,\n \"all_pairwise_distances\": [\n [0, 0, 0.426],\n [1, 1, 0.053]\n ],\n \"unassigned_y_true_idxs\": [0],\n \"unassigned_y_pred_idxs\": [0]\n}\n"The minimum confidence rating for this block. For regression, this is the absolute error (which can be larger than 1).
Structured outputs and computed metrics for some model types (e.g. object detection), one item per window.
Bounding boxes predicted by localization model
Labels predicted by localization model
Scores predicted by localization model
For object detection, the COCO mAP computed for the predictions on this image
For FOMO, the F1 score computed for the predictions on this image
An array with an expected label per window.
Start index of the label (e.g. 0)
End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
The label for this section.
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
Get classify job result, containing the result for the complete testing dataset.
Project ID
Whether to get only the classification results relevant to the feature explorer.
Keras model variant
Impulse ID. If this is unset then the default impulse is used.
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
2"idle01.d8Ae"Whether signature validation passed
true"HS256"Either the shared key or the public key that was used to validate the sample
Timestamp when the sample was created on device, or if no accurate time was known on device, the time that the file was processed by the ingestion service.
Timestamp when the sample was last modified.
"training""healthy-machine"Interval between two windows (1000 / frequency). If the data was resampled, then this lists the resampled interval.
16Frequency of the sample. If the data was resampled, then this lists the resampled frequency.
62.5Interval between two windows (1000 / frequency) in the source data (before resampling).
16Frequency of the sample in the source data (before resampling).
62.5Name of the axis
"accX"Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml).
Number of readings in this file
Total length (in ms.) of this file
Timestamp when the sample was added to the current acquisition bucket.
True if the current sample is excluded from use
True if the current sample is still processing (e.g. for video)
Set when sample is processing and a job has picked up the request
Set when processing this sample failed
Error (only set when processing this sample failed)
Whether the sample is cropped from another sample (and has crop start / end info)
Sample free form associated metadata
Unique identifier of the project this sample belongs to
Name of the owner of the project this sample belongs to
Name of the project this sample belongs to
What labeling flow the project this sample belongs to uses
Data sample SHA 256 hash (including CBOR envelope if applicable)
Start index of the label (e.g. 0)
End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
The label for this section.
If this sample was created by a synthetic data job, it's referenced here.
Identifier for this block. Make sure to up this number when creating a new block, and don't re-use identifiers. If the block hasn't changed, keep the ID as-is. ID must be unique across the project and greather than zero (>0).
The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression). Each behaves differently.
Block name, will be used in menus. If a block has a baseBlockId, this field is ignored and the base block's name is used instead.
"NN Classifier"DSP dependencies, identified by DSP block ID
27Block title, used in the impulse UI
"Classification (Keras)"A short description of the block version, displayed in the block versioning UI
"Reduced learning rate and more layers"The system component that created the block version (createImpulse | clone | tuner). Cannot be set via API.
"createImpulse"The datetime that the block version was created. Cannot be set via API.
Classification result, one item per window.
Anomaly scores and computed metrics for visual anomaly detection, one item per window.
For visual anomaly detection. An array of bounding box objects, (x, y, width, height, score, label), one per detection in the image. Filtered by the minimum confidence rating of the learn block.
2D array of shape (n, n) with raw anomaly scores for visual anomaly detection, where n can be calculated as ((1/8 of image input size)/2 - 1). The scores corresponds to each grid cell in the image's spatial matrix.
Mean value of the scores.
Maximum value of the scores.
Results of inferencing that returns structured data, such as object detection
For object detection. An array of bounding box arrays, (x, y, width, height), one per detection in the image.
For object detection. An array of labels, one per detection in the image.
For object detection. An array of probability scores, one per detection in the image.
For object detection. A score that indicates accuracy compared to the ground truth, if available.
For FOMO. A score that combines the precision and recall of a classifier into a single metric, if available.
A measure of how many of the positive predictions made are correct (true positives).
A measure of how many of the positive cases the classifier correctly predicted, over all the positive cases.
Debug info in JSON format
"{\n \"y_trues\": [\n {\"x\": 0.854, \"y\": 0.453125, \"label\": 1},\n {\"x\": 0.197, \"y\": 0.53125, \"label\": 2}\n ],\n \"y_preds\": [\n {\"x\": 0.916, \"y\": 0.875, \"label\": 1},\n {\"x\": 0.25, \"y\": 0.541, \"label\": 2}\n ],\n \"assignments\": [\n {\"yp\": 1, \"yt\": 1, \"label\": 2, \"distance\": 0.053}\n ],\n \"normalised_min_distance\": 0.2,\n \"all_pairwise_distances\": [\n [0, 0, 0.426],\n [1, 1, 0.053]\n ],\n \"unassigned_y_true_idxs\": [0],\n \"unassigned_y_pred_idxs\": [0]\n}\n"The minimum confidence rating for this block. For regression, this is the absolute error (which can be larger than 1).
Structured outputs and computed metrics for some model types (e.g. object detection), one item per window.
Bounding boxes predicted by localization model
Labels predicted by localization model
Scores predicted by localization model
For object detection, the COCO mAP computed for the predictions on this image
For FOMO, the F1 score computed for the predictions on this image
An array with an expected label per window.
Start index of the label (e.g. 0)
End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
The label for this section.
Only set for object detection projects
Only set for visual anomaly projects. 2D array of shape (n, n) with raw anomaly scores, where n varies based on the image input size and the specific visual anomaly algorithm used. The scores corresponds to each grid cell in the image's spatial matrix.
List of all model variants for which classification results exist
Classify a complete file against the specified learn block. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted. Depending on the size of your file, whether your sample is resampled, and whether the result is cached you'll get either the result or a job back. If you receive a job, then wait for the completion of the job, and then call this function again to receive the results. The unoptimized (float32) model is used by default, and classification with an optimized (int8) model can be slower.
Project ID
Sample ID
Block ID
Keras model variant
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Identifier for this block. Make sure to up this number when creating a new block, and don't re-use identifiers. If the block hasn't changed, keep the ID as-is. ID must be unique across the project and greather than zero (>0).
The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression). Each behaves differently.
Block name, will be used in menus. If a block has a baseBlockId, this field is ignored and the base block's name is used instead.
"NN Classifier"DSP dependencies, identified by DSP block ID
27Block title, used in the impulse UI
"Classification (Keras)"A short description of the block version, displayed in the block versioning UI
"Reduced learning rate and more layers"The system component that created the block version (createImpulse | clone | tuner). Cannot be set via API.
"createImpulse"The datetime that the block version was created. Cannot be set via API.
Classification result, one item per window.
Anomaly scores and computed metrics for visual anomaly detection, one item per window.
For visual anomaly detection. An array of bounding box objects, (x, y, width, height, score, label), one per detection in the image. Filtered by the minimum confidence rating of the learn block.
2D array of shape (n, n) with raw anomaly scores for visual anomaly detection, where n can be calculated as ((1/8 of image input size)/2 - 1). The scores corresponds to each grid cell in the image's spatial matrix.
Mean value of the scores.
Maximum value of the scores.
Results of inferencing that returns structured data, such as object detection
For object detection. An array of bounding box arrays, (x, y, width, height), one per detection in the image.
For object detection. An array of labels, one per detection in the image.
For object detection. An array of probability scores, one per detection in the image.
For object detection. A score that indicates accuracy compared to the ground truth, if available.
For FOMO. A score that combines the precision and recall of a classifier into a single metric, if available.
A measure of how many of the positive predictions made are correct (true positives).
A measure of how many of the positive cases the classifier correctly predicted, over all the positive cases.
Debug info in JSON format
"{\n \"y_trues\": [\n {\"x\": 0.854, \"y\": 0.453125, \"label\": 1},\n {\"x\": 0.197, \"y\": 0.53125, \"label\": 2}\n ],\n \"y_preds\": [\n {\"x\": 0.916, \"y\": 0.875, \"label\": 1},\n {\"x\": 0.25, \"y\": 0.541, \"label\": 2}\n ],\n \"assignments\": [\n {\"yp\": 1, \"yt\": 1, \"label\": 2, \"distance\": 0.053}\n ],\n \"normalised_min_distance\": 0.2,\n \"all_pairwise_distances\": [\n [0, 0, 0.426],\n [1, 1, 0.053]\n ],\n \"unassigned_y_true_idxs\": [0],\n \"unassigned_y_pred_idxs\": [0]\n}\n"The minimum confidence rating for this block. For regression, this is the absolute error (which can be larger than 1).
Structured outputs and computed metrics for some model types (e.g. object detection), one item per window.
Bounding boxes predicted by localization model
Labels predicted by localization model
Scores predicted by localization model
For object detection, the COCO mAP computed for the predictions on this image
For FOMO, the F1 score computed for the predictions on this image
An array with an expected label per window.
Start index of the label (e.g. 0)
End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
The label for this section.
2"idle01.d8Ae"Whether signature validation passed
true"HS256"Either the shared key or the public key that was used to validate the sample
Timestamp when the sample was created on device, or if no accurate time was known on device, the time that the file was processed by the ingestion service.
Timestamp when the sample was last modified.
"training""healthy-machine"Interval between two windows (1000 / frequency). If the data was resampled, then this lists the resampled interval.
16Frequency of the sample. If the data was resampled, then this lists the resampled frequency.
62.5Interval between two windows (1000 / frequency) in the source data (before resampling).
16Frequency of the sample in the source data (before resampling).
62.5Name of the axis
"accX"Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml).
Number of readings in this file
Total length (in ms.) of this file
Timestamp when the sample was added to the current acquisition bucket.
True if the current sample is excluded from use
True if the current sample is still processing (e.g. for video)
Set when sample is processing and a job has picked up the request
Set when processing this sample failed
Error (only set when processing this sample failed)
Whether the sample is cropped from another sample (and has crop start / end info)
Sample free form associated metadata
Unique identifier of the project this sample belongs to
Name of the owner of the project this sample belongs to
Name of the project this sample belongs to
What labeling flow the project this sample belongs to uses
Data sample SHA 256 hash (including CBOR envelope if applicable)
Start index of the label (e.g. 0)
End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
The label for this section.
If this sample was created by a synthetic data job, it's referenced here.
Sensor readings and metadata
Unique identifier for this device. Only set this when the device has a globally unique identifier (e.g. MAC address).
"ac:87:a3:0a:2d:1b"Device type, for example the exact model of the device. Should be the same for all similar devices.
"DISCO-L475VG-IOT01A"Array with sensor axes
Name of the axis
"accX"Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml).
Array of sensor values. One array item per interval, and as many items in this array as there are sensor axes. This type is returned if there are multiple axes.
New start index of the cropped sample
0New end index of the cropped sample
128Total number of payload values
Size of the sliding window (as set by the impulse) in milliseconds.
2996Number of milliseconds that the sliding window increased with (as set by the impulse)
10Whether this sample is already in the training database
Classify a complete file against the current impulse. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted. Depending on the size of your file, whether your sample is resampled, and whether the result is cached you'll get either the result or a job back. If you receive a job, then wait for the completion of the job, and then call this function again to receive the results. The unoptimized (float32) model is used by default, and classification with an optimized (int8) model can be slower.
Project ID
Sample ID
Whether to return the debug information from FOMO classification.
Keras model variant
Impulse ID. If this is unset then the default impulse is used.
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Identifier for this block. Make sure to up this number when creating a new block, and don't re-use identifiers. If the block hasn't changed, keep the ID as-is. ID must be unique across the project and greather than zero (>0).
The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression). Each behaves differently.
Block name, will be used in menus. If a block has a baseBlockId, this field is ignored and the base block's name is used instead.
"NN Classifier"DSP dependencies, identified by DSP block ID
27Block title, used in the impulse UI
"Classification (Keras)"A short description of the block version, displayed in the block versioning UI
"Reduced learning rate and more layers"The system component that created the block version (createImpulse | clone | tuner). Cannot be set via API.
"createImpulse"The datetime that the block version was created. Cannot be set via API.
Classification result, one item per window.
Anomaly scores and computed metrics for visual anomaly detection, one item per window.
For visual anomaly detection. An array of bounding box objects, (x, y, width, height, score, label), one per detection in the image. Filtered by the minimum confidence rating of the learn block.
2D array of shape (n, n) with raw anomaly scores for visual anomaly detection, where n can be calculated as ((1/8 of image input size)/2 - 1). The scores corresponds to each grid cell in the image's spatial matrix.
Mean value of the scores.
Maximum value of the scores.
Results of inferencing that returns structured data, such as object detection
For object detection. An array of bounding box arrays, (x, y, width, height), one per detection in the image.
For object detection. An array of labels, one per detection in the image.
For object detection. An array of probability scores, one per detection in the image.
For object detection. A score that indicates accuracy compared to the ground truth, if available.
For FOMO. A score that combines the precision and recall of a classifier into a single metric, if available.
A measure of how many of the positive predictions made are correct (true positives).
A measure of how many of the positive cases the classifier correctly predicted, over all the positive cases.
Debug info in JSON format
"{\n \"y_trues\": [\n {\"x\": 0.854, \"y\": 0.453125, \"label\": 1},\n {\"x\": 0.197, \"y\": 0.53125, \"label\": 2}\n ],\n \"y_preds\": [\n {\"x\": 0.916, \"y\": 0.875, \"label\": 1},\n {\"x\": 0.25, \"y\": 0.541, \"label\": 2}\n ],\n \"assignments\": [\n {\"yp\": 1, \"yt\": 1, \"label\": 2, \"distance\": 0.053}\n ],\n \"normalised_min_distance\": 0.2,\n \"all_pairwise_distances\": [\n [0, 0, 0.426],\n [1, 1, 0.053]\n ],\n \"unassigned_y_true_idxs\": [0],\n \"unassigned_y_pred_idxs\": [0]\n}\n"The minimum confidence rating for this block. For regression, this is the absolute error (which can be larger than 1).
Structured outputs and computed metrics for some model types (e.g. object detection), one item per window.
Bounding boxes predicted by localization model
Labels predicted by localization model
Scores predicted by localization model
For object detection, the COCO mAP computed for the predictions on this image
For FOMO, the F1 score computed for the predictions on this image
An array with an expected label per window.
Start index of the label (e.g. 0)
End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
The label for this section.
2"idle01.d8Ae"Whether signature validation passed
true"HS256"Either the shared key or the public key that was used to validate the sample
Timestamp when the sample was created on device, or if no accurate time was known on device, the time that the file was processed by the ingestion service.
Timestamp when the sample was last modified.
"training""healthy-machine"Interval between two windows (1000 / frequency). If the data was resampled, then this lists the resampled interval.
16Frequency of the sample. If the data was resampled, then this lists the resampled frequency.
62.5Interval between two windows (1000 / frequency) in the source data (before resampling).
16Frequency of the sample in the source data (before resampling).
62.5Name of the axis
"accX"Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml).
Number of readings in this file
Total length (in ms.) of this file
Timestamp when the sample was added to the current acquisition bucket.
True if the current sample is excluded from use
True if the current sample is still processing (e.g. for video)
Set when sample is processing and a job has picked up the request
Set when processing this sample failed
Error (only set when processing this sample failed)
Whether the sample is cropped from another sample (and has crop start / end info)
Sample free form associated metadata
Unique identifier of the project this sample belongs to
Name of the owner of the project this sample belongs to
Name of the project this sample belongs to
What labeling flow the project this sample belongs to uses
Data sample SHA 256 hash (including CBOR envelope if applicable)
Start index of the label (e.g. 0)
End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
The label for this section.
If this sample was created by a synthetic data job, it's referenced here.
Sensor readings and metadata
Unique identifier for this device. Only set this when the device has a globally unique identifier (e.g. MAC address).
"ac:87:a3:0a:2d:1b"Device type, for example the exact model of the device. Should be the same for all similar devices.
"DISCO-L475VG-IOT01A"Array with sensor axes
Name of the axis
"accX"Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml).
Array of sensor values. One array item per interval, and as many items in this array as there are sensor axes. This type is returned if there are multiple axes.
New start index of the cropped sample
0New end index of the cropped sample
128Total number of payload values
Size of the sliding window (as set by the impulse) in milliseconds.
2996Number of milliseconds that the sliding window increased with (as set by the impulse)
10Whether this sample is already in the training database
Get raw sample features for a particular window. This is only available after a live classification job has completed and raw features have been cached.
Project ID
Sample ID
Sample window index
Impulse ID. If this is unset then the default impulse is used.
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
2"idle01.d8Ae"Whether signature validation passed
true"HS256"Either the shared key or the public key that was used to validate the sample
Timestamp when the sample was created on device, or if no accurate time was known on device, the time that the file was processed by the ingestion service.
Timestamp when the sample was last modified.
"training""healthy-machine"Interval between two windows (1000 / frequency). If the data was resampled, then this lists the resampled interval.
16Frequency of the sample. If the data was resampled, then this lists the resampled frequency.
62.5Interval between two windows (1000 / frequency) in the source data (before resampling).
16Frequency of the sample in the source data (before resampling).
62.5Name of the axis
"accX"Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml).
Number of readings in this file
Total length (in ms.) of this file
Timestamp when the sample was added to the current acquisition bucket.
True if the current sample is excluded from use
True if the current sample is still processing (e.g. for video)
Set when sample is processing and a job has picked up the request
Set when processing this sample failed
Error (only set when processing this sample failed)
Whether the sample is cropped from another sample (and has crop start / end info)
Sample free form associated metadata
Unique identifier of the project this sample belongs to
Name of the owner of the project this sample belongs to
Name of the project this sample belongs to
What labeling flow the project this sample belongs to uses
Data sample SHA 256 hash (including CBOR envelope if applicable)
Start index of the label (e.g. 0)
End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
The label for this section.
If this sample was created by a synthetic data job, it's referenced here.
Sensor readings and metadata
Unique identifier for this device. Only set this when the device has a globally unique identifier (e.g. MAC address).
"ac:87:a3:0a:2d:1b"Device type, for example the exact model of the device. Should be the same for all similar devices.
"DISCO-L475VG-IOT01A"Array with sensor axes
Name of the axis
"accX"Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml).
Array of sensor values. One array item per interval, and as many items in this array as there are sensor axes. This type is returned if there are multiple axes.
New start index of the cropped sample
0New end index of the cropped sample
128Total number of payload values
Classify a complete file against the current impulse, for all given variants. Depending on the size of your file and whether the sample is resampled, you may get a job ID in the response.
Project ID
Sample ID
Whether to return the debug information from FOMO classification.
List of keras model variants, given as a JSON string
"[\"int8\", \"float32\"]"Impulse ID. If this is unset then the default impulse is used.
OK
Whether the operation succeeded
Optional error description (set if 'success' was false)
Identifier for this block. Make sure to up this number when creating a new block, and don't re-use identifiers. If the block hasn't changed, keep the ID as-is. ID must be unique across the project and greather than zero (>0).
The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression). Each behaves differently.
Block name, will be used in menus. If a block has a baseBlockId, this field is ignored and the base block's name is used instead.
"NN Classifier"DSP dependencies, identified by DSP block ID
27Block title, used in the impulse UI
"Classification (Keras)"A short description of the block version, displayed in the block versioning UI
"Reduced learning rate and more layers"The system component that created the block version (createImpulse | clone | tuner). Cannot be set via API.
"createImpulse"The datetime that the block version was created. Cannot be set via API.
Classification result, one item per window.
Anomaly scores and computed metrics for visual anomaly detection, one item per window.
For visual anomaly detection. An array of bounding box objects, (x, y, width, height, score, label), one per detection in the image. Filtered by the minimum confidence rating of the learn block.
2D array of shape (n, n) with raw anomaly scores for visual anomaly detection, where n can be calculated as ((1/8 of image input size)/2 - 1). The scores corresponds to each grid cell in the image's spatial matrix.
Mean value of the scores.
Maximum value of the scores.
Results of inferencing that returns structured data, such as object detection
For object detection. An array of bounding box arrays, (x, y, width, height), one per detection in the image.
For object detection. An array of labels, one per detection in the image.
For object detection. An array of probability scores, one per detection in the image.
For object detection. A score that indicates accuracy compared to the ground truth, if available.
For FOMO. A score that combines the precision and recall of a classifier into a single metric, if available.
A measure of how many of the positive predictions made are correct (true positives).
A measure of how many of the positive cases the classifier correctly predicted, over all the positive cases.
Debug info in JSON format
"{\n \"y_trues\": [\n {\"x\": 0.854, \"y\": 0.453125, \"label\": 1},\n {\"x\": 0.197, \"y\": 0.53125, \"label\": 2}\n ],\n \"y_preds\": [\n {\"x\": 0.916, \"y\": 0.875, \"label\": 1},\n {\"x\": 0.25, \"y\": 0.541, \"label\": 2}\n ],\n \"assignments\": [\n {\"yp\": 1, \"yt\": 1, \"label\": 2, \"distance\": 0.053}\n ],\n \"normalised_min_distance\": 0.2,\n \"all_pairwise_distances\": [\n [0, 0, 0.426],\n [1, 1, 0.053]\n ],\n \"unassigned_y_true_idxs\": [0],\n \"unassigned_y_pred_idxs\": [0]\n}\n"The minimum confidence rating for this block. For regression, this is the absolute error (which can be larger than 1).
Structured outputs and computed metrics for some model types (e.g. object detection), one item per window.
Bounding boxes predicted by localization model
Labels predicted by localization model
Scores predicted by localization model
For object detection, the COCO mAP computed for the predictions on this image
For FOMO, the F1 score computed for the predictions on this image
An array with an expected label per window.
Start index of the label (e.g. 0)
End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
The label for this section.
2"idle01.d8Ae"Whether signature validation passed
true"HS256"Either the shared key or the public key that was used to validate the sample
Timestamp when the sample was created on device, or if no accurate time was known on device, the time that the file was processed by the ingestion service.
Timestamp when the sample was last modified.
"training""healthy-machine"Interval between two windows (1000 / frequency). If the data was resampled, then this lists the resampled interval.
16Frequency of the sample. If the data was resampled, then this lists the resampled frequency.
62.5Interval between two windows (1000 / frequency) in the source data (before resampling).
16Frequency of the sample in the source data (before resampling).
62.5Name of the axis
"accX"Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml).
Number of readings in this file
Total length (in ms.) of this file
Timestamp when the sample was added to the current acquisition bucket.
True if the current sample is excluded from use
True if the current sample is still processing (e.g. for video)
Set when sample is processing and a job has picked up the request
Set when processing this sample failed
Error (only set when processing this sample failed)
Whether the sample is cropped from another sample (and has crop start / end info)
Sample free form associated metadata
Unique identifier of the project this sample belongs to
Name of the owner of the project this sample belongs to
Name of the project this sample belongs to
What labeling flow the project this sample belongs to uses
Data sample SHA 256 hash (including CBOR envelope if applicable)
Start index of the label (e.g. 0)
End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
The label for this section.
If this sample was created by a synthetic data job, it's referenced here.
Sensor readings and metadata
Unique identifier for this device. Only set this when the device has a globally unique identifier (e.g. MAC address).
"ac:87:a3:0a:2d:1b"Device type, for example the exact model of the device. Should be the same for all similar devices.
"DISCO-L475VG-IOT01A"Array with sensor axes
Name of the axis
"accX"Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml).
Array of sensor values. One array item per interval, and as many items in this array as there are sensor axes. This type is returned if there are multiple axes.
New start index of the cropped sample
0New end index of the cropped sample
128Total number of payload values
Size of the sliding window (as set by the impulse) in milliseconds.
2996Number of milliseconds that the sliding window increased with (as set by the impulse)
10Whether this sample is already in the training database