> ## Documentation Index > Fetch the complete documentation index at: https://docs.edgeimpulse.com/llms.txt > Use this file to discover all available pages before exploring further. # Get all impulses (incl. metrics) > Retrieve all impulse for a project, including accuracy and performance metrics. ## OpenAPI ````yaml /.assets/openapi.yaml get /api/{projectId}/impulses-detailed openapi: 3.0.0 info: title: Edge Impulse API version: 1.0.0 servers: - url: https://studio.edgeimpulse.com/v1 security: - ApiKeyAuthentication: [] - JWTAuthentication: [] - JWTHttpHeaderAuthentication: [] - OAuth2: [] paths: /api/{projectId}/impulses-detailed: get: tags: - Impulse summary: Get all impulses (incl. metrics) description: >- Retrieve all impulse for a project, including accuracy and performance metrics. operationId: getAllDetailedImpulses parameters: - $ref: '#/components/parameters/ProjectIdParameter' responses: '200': description: OK content: application/json: schema: $ref: '#/components/schemas/GetAllDetailedImpulsesResponse' components: parameters: ProjectIdParameter: name: projectId in: path required: true description: Project ID schema: type: integer schemas: GetAllDetailedImpulsesResponse: allOf: - $ref: '#/components/schemas/GenericApiResponse' - type: object required: - impulses - metricKeysByCategory - extraTableColumns properties: impulses: type: array items: $ref: '#/components/schemas/DetailedImpulse' metricKeysByCategory: type: array items: type: object required: - category - metricKeys properties: category: $ref: '#/components/schemas/DetailedImpulseMetricCategory' metricKeys: type: array items: type: object required: - name - description - type - showInTable properties: name: type: string description: type: string type: type: string enum: - core - additional filteringType: $ref: >- #/components/schemas/DetailedImpulseMetricFilteringType showInTable: type: boolean extraTableColumns: type: array description: >- Which extra impulse information should be shown in the impulses table. items: type: string GenericApiResponse: type: object required: - success properties: success: type: boolean description: Whether the operation succeeded error: type: string description: Optional error description (set if 'success' was false) DetailedImpulse: type: object required: - impulse - metrics - dspBlockConfigs - learnBlockKerasConfigs - learnBlockAnomalyConfigs - postProcessingBlockConfigs - configured - complete - isStale - tags properties: impulse: $ref: '#/components/schemas/Impulse' metrics: type: array items: $ref: '#/components/schemas/DetailedImpulseMetric' dspBlockConfigs: type: array items: type: object required: - blockId - config properties: blockId: type: integer config: $ref: '#/components/schemas/DSPConfig' description: >- This returns a DSPConfig object, but "dsp.classes" and "dsp.features.classes" will be set to an empty array (use getDspConfig to retrieve these). metadata: $ref: '#/components/schemas/DSPMetadata' description: >- This returns a DSPMetadata object, but "labels" will be set to an empty array (use getDspMetadata to retrieve these). learnBlockKerasConfigs: items: type: object required: - blockId - config properties: blockId: type: integer config: $ref: '#/components/schemas/KerasConfig' description: >- This returns a KerasConfig object, but "transferLearningModels" and "dependencies.classes" will be set to an empty array (use getKeras to retrieve these). metadata: $ref: '#/components/schemas/KerasModelMetadata' description: >- This returns a KerasModelMetadata object, but 1) non-default "onDevicePerformance", 2) "predictions", 3) "labels"; are omitted (use getKerasMetadata to retrieve these). learnBlockAnomalyConfigs: items: type: object required: - blockId - config properties: blockId: type: integer config: $ref: '#/components/schemas/AnomalyConfig' metadata: $ref: '#/components/schemas/AnomalyModelMetadata' description: >- This returns a AnomalyModelMetadata object, but 1) non-default "onDevicePerformance", 2) "predictions" are omitted (use getAnomalyMetadata to retrieve these). gmmMetadata: $ref: '#/components/schemas/AnomalyGmmMetadata' postProcessingBlockConfigs: items: type: object required: - blockId - config properties: blockId: type: integer config: $ref: '#/components/schemas/PostProcessingConfig' pretrainedModelInfo: type: object required: - fileName properties: fileName: type: string isStale: type: boolean description: >- Whether this impulse contains blocks with "stale" features (i.e. the dataset has changed since features were generated) configured: type: boolean description: Whether this impulse is configured complete: type: boolean description: Whether this impulse is fully trained and configured tags: type: array items: type: string description: Tags associated with this impulse createdFromTunerTrialId: type: number description: >- The source EON Tuner trial ID for impulses created from the EON Tuner createdByUser: $ref: '#/components/schemas/CreatedUpdatedByUser' description: The user who originally created this impulse. DetailedImpulseMetricCategory: type: string enum: - impulseMetrics - inputBlockConfig - dspBlockConfig - learnBlockConfig - learnBlockMetrics - postProcessingBlockConfig DetailedImpulseMetricFilteringType: type: object required: - type - options properties: type: type: string enum: - numeric - string - select - boolean - list options: type: array items: type: string Impulse: type: object required: - id - name - inputBlocks - dspBlocks - learnBlocks - postProcessingBlocks - type properties: id: type: integer description: ID for this impulse. name: type: string description: Name for this impulse. inputBlocks: type: array description: Input Blocks that are part of this impulse items: $ref: '#/components/schemas/ImpulseInputBlock' dspBlocks: type: array description: DSP Blocks that are part of this impulse items: $ref: '#/components/schemas/ImpulseDspBlock' learnBlocks: type: array description: Learning Blocks that are part of this impulse items: $ref: '#/components/schemas/ImpulseLearnBlock' postProcessingBlocks: type: array description: Post-processing blocks that are part of this impulse items: $ref: '#/components/schemas/ImpulsePostProcessingBlock' type: $ref: '#/components/schemas/ImpulseType' DetailedImpulseMetric: type: object required: - name - type - category - description - value properties: name: type: string type: type: string enum: - core - additional filteringType: $ref: '#/components/schemas/DetailedImpulseMetricFilteringType' category: $ref: '#/components/schemas/DetailedImpulseMetricCategory' description: type: string value: oneOf: - type: string - type: boolean title: type: string valueForSorting: type: integer valueHint: type: string description: Additional help explaining the value for this metric DSPConfig: type: object required: - dsp properties: dsp: $ref: '#/components/schemas/DSPInfo' config: type: array items: $ref: '#/components/schemas/DSPGroup' configError: type: string DSPMetadata: type: object required: - created - generated - dspConfig - labels - featureCount - windowCount - includedSamples - windowSizeMs - windowIncreaseMs - padZeros - frequency - outputConfig properties: created: type: string format: date-time description: Date when the features were created generated: type: boolean description: Whether features were generated dspConfig: type: object additionalProperties: type: string labels: type: array description: Labels in the dataset when generator ran items: type: string featureLabels: type: array description: >- Names of the generated features. Only set if axes have explicit labels. items: type: string windowCount: type: integer featureCount: type: integer description: Number of features for this DSP block includedSamples: type: array description: >- The included samples in this DSP block. Note that these are sorted in the same way as the `npy` files are laid out. So with the `windowCount` parameter you can exactly search back to see which file contributed to which windows there. items: type: object required: - id - windowCount properties: id: type: integer windowCount: type: integer windowSizeMs: type: integer description: Length of the sliding window when generating features. windowIncreaseMs: type: integer description: Increase of the sliding window when generating features. padZeros: type: boolean description: Whether data was zero-padded when generating features. frequency: type: number description: Frequency of the original data in Hz. outputConfig: type: object description: Information about the output of the DSP block required: - type - shape properties: type: type: string description: Output type of the DSP block enum: - image - spectrogram - flat shape: type: object description: The shape of the block output required: - width properties: width: description: >- Available on all types. Denotes the width of an 'image' or 'spectrogram', or the number of features in a 'flat' block. type: integer height: description: Only available for type 'image' and 'spectrogram' type: integer channels: description: Only available for type 'image' type: integer frames: description: Number of frames, only available for type 'image' type: integer fftUsed: type: array items: type: integer resamplingAlgorithmVersion: type: number description: >- The version number of the resampling algorithm used (for resampled time series data only) featureExplorerJobId: type: integer description: >- When specified, a job is running (asynchronously) to generate the feature explorer. featureExplorerJobFailed: description: >- If this is set, then the feature explorer job failed (get the status by getting the job logs for 'featureExplorerJobId'). type: boolean featureImportanceJobId: type: integer description: >- When specified, a job is running (asynchronously) to generate feature importance. featureImportanceJobFailed: description: >- If this is set, then the feature importance job failed (get the status by getting the job logs for 'featureImportanceJobId'). type: boolean KerasConfig: type: object required: - dependencies - trained - name - script - minimumConfidenceRating - selectedModelType - mode - trainingCycles - learningRate - defaultBatchSize - visualLayers - augmentationPolicyImage - transferLearningModels - shape - profileInt8 - skipEmbeddingsAndMemory - showAdvancedTrainingSettings - showAugmentationTrainingSettings - thresholds properties: dependencies: $ref: '#/components/schemas/DependencyData' trained: type: boolean description: Whether the block is trained name: type: string type: $ref: '#/components/schemas/LearnBlockType' script: type: string description: The Keras script. This script might be empty if the mode is visual. minimumConfidenceRating: type: number description: >- DEPRECATED, see "thresholds" instead. Minimum confidence rating required for the neural network. Scores below this confidence are tagged as uncertain. selectedModelType: $ref: '#/components/schemas/KerasModelTypeEnum' description: The model type that is currently selected. mode: type: string description: The mode (visual or expert) to use for editing this network. enum: - visual - expert visualLayers: type: array description: >- The visual layers (if in visual mode) for the neural network. This will be an empty array when in expert mode. items: $ref: '#/components/schemas/KerasVisualLayer' trainingCycles: type: integer description: Number of training cycles. If in expert mode this will be 0. learningRate: type: number description: Learning rate (between 0 and 1). If in expert mode this will be 0. batchSize: type: integer description: The batch size used during training. defaultBatchSize: type: integer description: The default batch size if a value is not configured. shape: type: string description: Python-formatted tuple of input axes trainTestSplit: type: number description: Train/test split (between 0 and 1) autoClassWeights: type: boolean description: >- Whether to automatically balance class weights, use this for skewed datasets. useLearnedOptimizer: type: boolean description: Use learned optimizer and ignore learning rate. augmentationPolicyImage: $ref: '#/components/schemas/AugmentationPolicyImageEnum' augmentationPolicySpectrogram: $ref: '#/components/schemas/AugmentationPolicySpectrogram' transferLearningModels: type: array items: $ref: '#/components/schemas/TransferLearningModel' profileInt8: type: boolean description: Whether to profile the i8 model (might take a very long time) skipEmbeddingsAndMemory: type: boolean description: >- If set, skips creating embeddings and measuring memory (used in tests) akidaEdgeLearningConfig: $ref: '#/components/schemas/AkidaEdgeLearningConfig' customValidationMetadataKey: type: string description: >- This metadata key is used to prevent group data leakage between train and validation datasets. showAdvancedTrainingSettings: type: boolean description: >- Whether the 'Advanced training settings' UI element should be expanded. showAugmentationTrainingSettings: type: boolean description: >- Whether the 'Augmentation training settings' UI element should be expanded. customParameters: type: object description: >- Training parameters, this list depends on the list of parameters that the model exposes. additionalProperties: type: string nullable: true anomalyCapacity: $ref: '#/components/schemas/AnomalyCapacity' description: >- Capacity level for visual anomaly detection (GMM). Determines which set of default configurations to use. The higher capacity, the higher number of (Gaussian) components, and the more adapted the model becomes to the original distribution lastShownModelVariant: $ref: '#/components/schemas/KerasModelVariantEnum' description: >- Last shown variant on the Keras screen. Used to keep the same view after refreshing. blockParameters: $ref: '#/components/schemas/BlockParameters' description: >- Training parameters specific to the type of the learn block. Parameters may be adjusted depending on the model defined in the visual layers. Used for our built-in blocks. thresholds: type: array description: List of configurable thresholds for this block. items: $ref: '#/components/schemas/BlockThreshold' KerasModelMetadata: type: object required: - created - layers - classNames - availableModelTypes - recommendedModelType - modelValidationMetrics - hasTrainedModel - mode - imageInputScaling - labels - thresholds properties: created: type: string format: date-time description: Date when the model was trained layers: type: array description: Layers of the neural network items: $ref: '#/components/schemas/KerasModelLayer' classNames: type: array description: Labels for the output layer items: type: string labels: type: array description: >- Original labels in the dataset when features were generated, e.g. used to render the feature explorer. items: type: string availableModelTypes: type: array description: The types of model that are available items: $ref: '#/components/schemas/KerasModelTypeEnum' recommendedModelType: $ref: '#/components/schemas/KerasModelTypeEnum' description: The model type that is recommended for use modelValidationMetrics: type: array description: Metrics for each of the available model types items: $ref: '#/components/schemas/KerasModelMetadataMetrics' hasTrainedModel: type: boolean mode: $ref: '#/components/schemas/KerasModelMode' objectDetectionLastLayer: $ref: '#/components/schemas/ObjectDetectionLastLayer' imageInputScaling: $ref: '#/components/schemas/ImageInputScaling' thresholds: type: array description: List of configurable thresholds for this block. items: $ref: '#/components/schemas/BlockThreshold' tensorboardGraphs: $ref: '#/components/schemas/TensorboardGraphs' description: List of TensorBoard graphs associated with this model AnomalyConfig: type: object required: - name - axes - trained - dependencies - selectedAxes - minimumConfidenceRating - thresholds properties: dependencies: $ref: '#/components/schemas/DependencyData' name: type: string axes: type: array description: Selectable axes for the anomaly detection block items: type: object required: - ix - label - selected - favourite properties: ix: type: integer label: type: string selected: type: boolean favourite: type: boolean trained: type: boolean description: Whether the block is trained clusterCount: type: integer description: >- Number of clusters for K-means, or number of components for GMM (in config) selectedAxes: type: array items: type: integer description: Selected clusters (in config) minimumConfidenceRating: type: number description: >- DEPRECATED, see "thresholds" instead. Minimum confidence rating for this block, scores above this number will be flagged as anomaly. thresholds: type: array description: List of configurable thresholds for this block. items: $ref: '#/components/schemas/BlockThreshold' AnomalyModelMetadata: type: object required: - created - scale - mean - clusters - axes - defaultMinimumConfidenceRating - thresholds properties: created: type: string format: date-time description: Date when the model was trained scale: type: array description: >- Scale input for StandardScaler. Values are scaled like this (where `ix` is axis index): `input[ix] = (input[ix] - mean[ix]) / scale[ix];` items: type: number mean: type: array description: >- Mean input for StandardScaler. Values are scaled like this (where `ix` is axis index): `input[ix] = (input[ix] - mean[ix]) / scale[ix];` items: type: number clusters: type: array description: Trained K-means clusters items: type: object required: - center - maxError properties: center: type: array description: Center of each cluster (one value per axis) items: type: number maxError: type: number description: Size of the cluster axes: type: array description: Which axes were included during training (by index) example: '`[ 0, 11, 22 ]`' items: type: integer defaultMinimumConfidenceRating: type: number description: >- DEPRECATED, see "thresholds" instead. Default minimum confidence rating required before tagging as anomaly, based on scores of training data (GMM only). availableModelTypes: type: array description: The types of model that are available items: $ref: '#/components/schemas/KerasModelTypeEnum' recommendedModelType: $ref: '#/components/schemas/KerasModelTypeEnum' description: The model type that is recommended for use modelValidationMetrics: type: array description: Metrics for each of the available model types items: $ref: '#/components/schemas/KerasModelMetadataMetrics' hasTrainedModel: type: boolean thresholds: type: array description: List of configurable thresholds for this block. items: $ref: '#/components/schemas/BlockThreshold' AnomalyGmmMetadata: type: object required: - means - covariances - weights properties: means: type: array items: type: array items: type: number description: 2D array of shape (n, m) covariances: type: array items: type: array items: type: array items: type: number description: 3D array of shape (n, m, m) weights: type: array items: type: number description: 1D array of shape (n,) PostProcessingConfig: type: object required: - enabled - parameters properties: enabled: type: boolean parameters: type: array items: $ref: '#/components/schemas/DSPGroupItem' CreatedUpdatedByUser: type: object required: - id - name - username properties: id: type: integer name: type: string username: type: string photo: type: string ImpulseInputBlock: type: object required: - id - type - name - title properties: id: type: integer minimum: 1 description: >- Identifier for this block. Make sure to up this number when creating a new block via `getNewBlockId`, 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). type: $ref: '#/components/schemas/InputBlockType' description: Block type (either time-series, image or features) example: time-series name: type: string description: Block name, will be used in menus example: Time series title: type: string description: Block title, used in the impulse UI example: Time series axes: type: array description: >- Input axes, identified by the name in the name of the axis. If not set, then all axes are selected. items: type: string example: accX windowSizeMs: type: integer description: Size of the sliding window in milliseconds example: 2004 windowIncreaseMs: type: integer description: >- We use a sliding window to go over the raw data. How many milliseconds to increase the sliding window with for each step. frequencyHz: type: number description: (Input only) Frequency of the input data in Hz example: 60 classificationWindowIncreaseMs: type: integer description: >- We use a sliding window to go over the raw data. How many milliseconds to increase the sliding window with for each step in classification mode. padZeros: type: boolean description: Whether to zero pad data when a data item is too short labelingMethodMultiLabel: type: object description: >- Determines how a single label is assigned to each generated window when source samples contain multiple labels. Available options - end-of-window: use the label active at the end timestamp of the window. - anywhere-in-window: assign one of the configured `labels` when it appears anywhere in the window. - anywhere-in-window-min-percentage: assign one of the configured `labels` only when it covers at least `minPercentage` of the full window. - majority-in-window: assign the label with the highest prevalence across the full window. Tie-breaking: - For count-based modes (anywhere-in-window, anywhere-in-window-min-percentage, and majority-in-window), if two or more candidate labels have equal prevalence, the first encountered label in the window is used. required: - type properties: type: type: string enum: - end-of-window - anywhere-in-window - anywhere-in-window-min-percentage - majority-in-window labels: description: >- Required when choosing "anywhere-in-window" or "anywhere-in-window-min-percentage". The list of classes that should trigger detection (e.g. "interference"). type: array items: type: string minPercentage: description: >- Required when choosing "anywhere-in-window-min-percentage". A label is assigned only if it is present in at least this percentage of the full window. type: number imageWidth: type: integer description: Width all images are resized to before training example: 28 imageHeight: type: integer description: Width all images are resized to before training example: 28 resizeMode: $ref: '#/components/schemas/ImageInputResizeMode' resizeMethod: type: string description: Resize method to use when resizing images example: squash enum: - lanczos3 - nearest cropAnchor: type: string description: If images are resized using a crop, choose where to anchor the crop example: middle-center enum: - top-left - top-center - top-right - middle-left - middle-center - middle-right - bottom-left - bottom-center - bottom-right createdBy: type: string description: >- The system component that created the block version (createImpulse | clone | tuner). Cannot be set via API. example: createImpulse createdAt: type: string format: date-time description: >- The datetime that the block version was created. Cannot be set via API. datasetSubset: $ref: '#/components/schemas/ImpulseInputBlockDatasetSubset' ImpulseDspBlock: type: object required: - id - type - name - axes - title - implementationVersion properties: id: type: integer minimum: 1 description: >- Identifier for this block. Make sure to up this number when creating a new block via `getNewBlockId`, 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). type: type: string description: Block type example: spectral-analysis name: type: string description: Block name, will be used in menus example: Spectral features axes: type: array description: Input axes, identified by the name in the name of the axis items: type: string example: accX title: type: string description: Block title, used in the impulse UI example: Spectral Analysis valuesPerAxis: type: integer description: >- Number of features this DSP block outputs per axis. This is only set when the DSP block is configured. example: 11 input: type: integer description: The ID of the Input block a DSP block is connected to example: 1 createdBy: type: string description: >- The system component that created the block version (createImpulse | clone | tuner). Cannot be set via API. example: createImpulse createdAt: type: string format: date-time description: >- The datetime that the block version was created. Cannot be set via API. implementationVersion: type: integer description: Implementation version of the block organization: type: object required: - id - dspId properties: id: type: integer dspId: type: integer customUrl: type: string description: Required for type 'custom' namedAxes: type: array description: Named axes for the block items: type: object required: - name properties: name: type: string description: Name of the axis description: type: string description: Description of the axis required: type: boolean description: Whether the axis is required selectedAxis: type: string description: The selected axis for the block ImpulseLearnBlock: type: object required: - id - type - name - dsp - title properties: id: type: integer minimum: 1 description: >- Identifier for this block. Make sure to up this number when creating a new block via `getNewBlockId`, 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). type: $ref: '#/components/schemas/LearnBlockType' name: type: string description: >- 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. example: NN Classifier dsp: type: array description: DSP dependencies, identified by DSP block ID items: type: integer example: 27 title: type: string description: Block title, used in the impulse UI example: Classification (Keras) createdBy: type: string description: >- The system component that created the block version (createImpulse | clone | tuner). Cannot be set via API. example: createImpulse createdAt: type: string format: date-time description: >- The datetime that the block version was created. Cannot be set via API. trainingProcessor: $ref: '#/components/schemas/ExperimentalImpulseSpecificGpuTrainingProcessor' ImpulsePostProcessingBlock: type: object required: - id - type - name - title - implementationVersion properties: id: type: integer minimum: 1 description: >- Identifier for this block. Make sure to up this number when creating a new block via `getNewBlockId`, 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). type: type: string description: Block type example: object-tracking name: type: string description: Block name, will be used in menus example: Object tracking title: type: string description: Block title, used in the impulse UI example: Object tracking createdBy: type: string description: >- The system component that created the block version (createImpulse | clone | tuner). Cannot be set via API. example: createImpulse createdAt: type: string format: date-time description: >- The datetime that the block version was created. Cannot be set via API. implementationVersion: type: integer description: Implementation version of the block ImpulseType: type: string description: > Specifies the type of impulse. Options include: - default: Standard Edge Impulse pipeline. - BYOM: Impulse that includes a pretrained model. - VLM: Impulse created as part of a Vision Learning Model (VLM) workflow. enum: - default - BYOM - VLM DSPInfo: type: object required: - id - name - windowLength - type - classes - features - expectedWindowCount - inputAxes - canCalculateFeatureImportance - calculateFeatureImportance - canNormalizeData - normalizeData properties: id: type: integer example: 1 name: type: string example: Spectral features windowLength: type: integer example: 3000 type: type: string example: spectral-analysis classes: type: array items: type: string features: type: object required: - generated properties: generated: type: boolean description: Whether this block has generated features count: type: integer description: Number of generated features labels: type: array description: Names of the features items: type: string classes: type: array description: Classes that the features were generated on items: type: string expectedWindowCount: type: integer description: Expected number of windows that would be generated inputAxes: type: array description: Axes that this block depends on. items: type: string performance: $ref: '#/components/schemas/DspPerformance' canCalculateFeatureImportance: type: boolean calculateFeatureImportance: type: boolean canNormalizeData: type: boolean description: >- Whether this DSP block supports data normalization after features were generated. This is true unless "dontAllowDataNormalization" is set to true in the DSP block's parameters.json. normalizeData: $ref: '#/components/schemas/DSPNormalizeData' description: Data normalization that was last selected for this block. hasAutoTune: description: Whether this type of DSP block supports autotuning. type: boolean minimumVersionForAutotune: description: >- For DSP blocks that support autotuning, this value specifies the minimum block implementation version for which autotuning is supported. type: number hasAutotunerResults: description: Whether autotune results exist for this DSP block. type: boolean usesState: description: Whether this DSP block uses state. type: boolean DSPGroup: type: object required: - group - items properties: group: type: string example: Scaling items: type: array items: $ref: '#/components/schemas/DSPGroupItem' DependencyData: type: object required: - classes - blockNames - featureCount - sampleCount - outputClasses properties: classes: type: array description: Set of all labels present in data feeding into this model items: type: string blockNames: type: array items: type: string featureCount: type: integer sampleCount: type: integer outputClasses: type: array description: Set of output classes for this model items: type: string LearnBlockType: type: string description: >- The type of learning block (anomaly, keras, keras-transfer-image, keras-transfer-kws, keras-object-detection, keras-regression, keras-freeform). Each behaves differently. enum: - anomaly - anomaly-gmm - keras - keras-transfer-image - keras-transfer-kws - keras-object-detection - keras-regression - keras-akida - keras-akida-transfer-image - keras-akida-object-detection - keras-visual-anomaly - keras-freeform - keras-anomaly - vlm KerasModelTypeEnum: type: string enum: - int8 - float32 - akida - requiresRetrain KerasVisualLayer: type: object required: - type properties: type: $ref: '#/components/schemas/KerasVisualLayerType' neurons: type: integer description: >- Number of neurons or filters in this layer (only for dense, conv1d, conv2d) or in the final conv2d layer (only for transfer layers) kernelSize: type: integer description: Kernel size for the convolutional layers (only for conv1d, conv2d) dropoutRate: type: number description: >- Fraction of input units to drop (only for dropout) or in the final layer dropout (only for transfer layers) columns: type: integer description: Number of columns for the reshape operation (only for reshape) stack: type: integer description: >- Number of convolutional layers before the pooling layer (only for conv1d, conv2d) enabled: type: boolean organizationModelId: type: integer description: >- Custom transfer learning model ID (when type is set to transfer_organization) AugmentationPolicyImageEnum: type: string description: The data augmentation policy to use with image input enum: - none - all AugmentationPolicySpectrogram: type: object required: - enabled properties: enabled: type: boolean description: >- True if spectrogram augmentation is enabled. Other properties will be ignored if this is false. warping: type: boolean description: True if warping along the time axis is enabled. freqMasking: type: string enum: - none - low - high description: The amount of frequency masking to apply. timeMasking: type: string enum: - none - low - high description: The amount of time masking to apply. gaussianNoise: type: string enum: - none - low - high description: The amount of Gaussian noise to add. TransferLearningModel: type: object required: - name - shortName - description - hasNeurons - hasDropout - type - learnBlockType - author - blockType - hasExpertMode properties: name: type: string shortName: type: string abbreviatedName: type: string description: type: string hasNeurons: type: boolean hasDropout: type: boolean defaultNeurons: type: integer defaultDropout: type: number defaultLearningRate: type: number defaultTrainingCycles: type: number hasImageAugmentation: type: boolean type: $ref: '#/components/schemas/KerasVisualLayerType' learnBlockType: $ref: '#/components/schemas/LearnBlockType' organizationModelId: type: integer implementationVersion: type: integer repositoryUrl: type: string description: URL to the source code of this custom learn block. author: type: string blockType: $ref: '#/components/schemas/BlockType' customParameters: type: array items: $ref: '#/components/schemas/DSPGroupItem' displayCategory: $ref: '#/components/schemas/BlockDisplayCategory' blockNoLongerAvailable: type: object description: >- If this object is set, then you can no longer train this block. The reason (or a migration path) is in the `reasonMarkdown` and `reasonHtml` properties. required: - reasonMarkdown - reasonHtml properties: reasonMarkdown: description: >- Reason or migration path for current users of this block, in Markdown format. type: string reasonHtml: description: >- Reason or migration path for current users of this block, in HTML format. type: string hasExpertMode: description: Whether this block can be switched to expert mode. type: boolean AkidaEdgeLearningConfig: type: object required: - enabled properties: enabled: type: boolean description: >- True if Akida Edge Learning model creation is enabled. Other properties will be ignored if this is false. additionalClasses: type: number description: >- Number of additional classes that will be added to the Edge Learning model. neuronsPerClass: type: number description: >- Number of neurons in each class on the last layer in the Edge Learning model. AnomalyCapacity: type: string description: >- Capacity level for visual anomaly detection. Determines which set of default configurations to use. The higher capacity, the higher number of (Gaussian) components, and the more adapted the model becomes to the original distribution enum: - low - medium - high KerasModelVariantEnum: type: string enum: - int8 - float32 - akida BlockParameters: description: >- Training parameters specific to the type of the learn block. Parameters may be adjusted depending on the model defined in the visual layers. Used for our built-in blocks. oneOf: - $ref: '#/components/schemas/BlockParamsVisualAnomalyPatchcore' - $ref: '#/components/schemas/BlockParamsVisualAnomalyGmm' BlockThreshold: type: object description: >- Configurable threshold for this block (e.g. minimum score before tagging as an anomaly, or the min. score to save bounding boxes) required: - key - description - helpText - value properties: key: type: string description: >- Identifier to reference the threshold. You'll need to refer to the threshold by this key when you set the threshold). example: min_score description: type: string description: User-friendly description of the threshold. example: Score threshold helpText: type: string description: Additional help text (shown in the UI under a "?" icon) example: >- Threshold score for bounding boxes. If the score for a bounding box is below this the box will be discarded. suggestedValue: type: number description: >- If the threshold has a suggested value, e.g. a max. absolute error for regression projects; or the min. anomaly score for visual anomaly detection, then this is the numeric value of that threshold. suggestedValueText: type: string description: >- If the threshold has a suggested value, e.g. a max. absolute error for regression projects; or the min. anomaly score for visual anomaly detection, then this is the stringified value of that threshold. value: description: Current value of the threshold example: 0.5 oneOf: - type: number - type: string dropdownOptions: description: Optional list of options, will be shown in a dropdown. type: array items: type: object required: - description - value properties: description: type: string description: Full description of the value value: type: string description: Value, maps back to "BlockThreshold#value" KerasModelLayer: type: object required: - input - output properties: input: type: object required: - shape - name - type properties: shape: type: integer description: Input size example: 33 name: type: string description: TensorFlow name example: x_input:0 type: type: string description: TensorFlow type example: '' output: type: object required: - shape - name - type properties: shape: type: integer description: Output size example: 20 name: type: string description: TensorFlow name example: dense_1/Relu:0 type: type: string description: TensorFlow type example: '' KerasModelMetadataMetrics: type: object required: - type - loss - confusionMatrix - report - onDevicePerformance - visualization - isSupportedOnMcu - additionalMetrics properties: type: $ref: '#/components/schemas/KerasModelTypeEnum' description: The type of model loss: type: number description: The model's loss on the validation set after training accuracy: type: number description: The model's accuracy on the validation set after training confusionMatrix: type: array example: - - 31 - 1 - 0 - - 2 - 27 - 3 - - 1 - 0 - 39 items: type: array items: type: number report: type: object description: Precision, recall, F1 and support scores onDevicePerformance: type: array items: type: object required: - mcu - name - isDefault - latency - tflite - eon - hasPerformance properties: mcu: type: string name: type: string isDefault: type: boolean latency: type: number tflite: type: object required: - ramRequired - romRequired - arenaSize - modelSize properties: ramRequired: type: integer romRequired: type: integer arenaSize: type: integer modelSize: type: integer eon: type: object required: - ramRequired - romRequired - arenaSize - modelSize properties: ramRequired: type: integer romRequired: type: integer arenaSize: type: integer modelSize: type: integer eon_ram_optimized: type: object required: - ramRequired - romRequired - arenaSize - modelSize properties: ramRequired: type: integer romRequired: type: integer arenaSize: type: integer modelSize: type: integer customMetrics: description: Custom, device-specific performance metrics type: array items: $ref: '#/components/schemas/KerasCustomMetric' hasPerformance: description: If false, then no metrics are available for this target type: boolean profilingError: description: Specific error during profiling (e.g. model not supported) type: string predictions: type: array items: $ref: '#/components/schemas/ModelPrediction' visualization: type: string enum: - featureExplorer - dataExplorer - none isSupportedOnMcu: type: boolean mcuSupportError: type: string profilingJobId: description: >- If this is set, then we're still profiling this model. Subscribe to job updates to see when it's done (afterward the metadata will be updated). type: integer profilingJobFailed: description: >- If this is set, then the profiling job failed (get the status by getting the job logs for 'profilingJobId'). type: boolean additionalMetrics: type: array items: $ref: '#/components/schemas/AdditionalMetric' KerasModelMode: type: string enum: - classification - regression - object-detection - visual-anomaly - anomaly-gmm - freeform - anomaly ObjectDetectionLastLayer: type: string enum: - mobilenet-ssd - fomo - yolov2-akida - yolov5 - yolov5v5-drpai - yolox - yolov7 - yolo-pro - tao-retinanet - tao-ssd - tao-yolov3 - tao-yolov4 - yolov11 - yolov11-abs - paddleocr-detector - qc-face-det-lite ImageInputScaling: description: >- Normalization that is applied to images. If this is not set then 0..1 is used. "0..1" gives you non-normalized pixels between 0 and 1. "-1..1" gives you non-normalized pixels between -1 and 1. "0..255" gives you non-normalized pixels between 0 and 255. "-128..127" gives you non-normalized pixels between -128 and 127. "torch" first scales pixels between 0 and 1, then applies normalization using the ImageNet dataset (same as `torchvision.transforms.Normalize()`). "bgr-subtract-imagenet-mean" scales to 0..255, reorders pixels to BGR, and subtracts the ImageNet mean from each channel. type: string enum: - 0..1 - '-1..1' - '-128..127' - 0..255 - torch - bgr-subtract-imagenet-mean TensorboardGraphs: type: array items: $ref: '#/components/schemas/KerasModelMetadataGraph' DSPGroupItem: type: object required: - name - type - param - defaultValue - readonly - shouldShow - required - showClickToSet properties: name: type: string example: Scale axes value: type: string defaultValue: type: string type: type: string example: text help: type: string example: Divide axes by this number param: type: string example: scale-axes selectOptions: type: array items: type: object properties: value: type: string description: >- What is the string that will be set if this option is selected? selected: type: boolean optionLabel: type: string description: >- What is the label that will be shown to the user for this option? priority: type: number description: >- The following options are optional. See Learn Block Auto Config in Notion. Higher priority will get chosen based on limits below. romEstimate: type: number description: >- Estimated ROM footprint for this choice. Will be tested against ROM budget in Studio. needsOps: type: array items: type: string description: ML operator needed by this choice. needsFeatures: type: array items: type: string description: Feature needed by this choice. (non op related) readonly: type: boolean shouldShow: type: boolean showIf: type: object required: - parameter - operator - value properties: parameter: type: string operator: type: string enum: - eq - neq value: type: string invalidText: type: string section: type: string description: Interface section to render parameter in. enum: - advanced - augmentation - modelProfiling multiline: type: boolean description: Only valid for type "string". Will render a multiline text area. required: type: boolean hint: type: string description: If set, shows a hint below the input. placeholder: type: string description: >- Sets the placeholder text on the input element (for types "string", "int", "float" and "secret") showClickToSet: type: boolean description: If enabled, render a disabled input element with 'Click to set' valid: type: array items: type: object description: Valid values for parameter. items: type: object description: Recursive definition for items of a parameter with type 'array'. properties: type: object description: Recursive definition for a parameter with type 'object'. minVal: type: number description: Minimum value for parameters of type 'int' or 'float'. maxVal: type: number description: Maximum value for parameters of type 'int' or 'float'. InputBlockType: type: string enum: - time-series - image - features ImageInputResizeMode: type: string description: > Input images are resized automatically before training and testing, to match the impulse input shape. This determines the resize mode used when the aspect ratio of the input data is different to the aspect ratio of the impulse. example: squash enum: - squash - fit-short - fit-long - crop ImpulseInputBlockDatasetSubset: type: object description: >- Only generate features for samples where (sample_id + datasetSubsetSeed) % datasetSubset) == 0 required: - includePercentage properties: includePercentage: description: >- Number between 0 and 100, with the % of data that should be _included_ type: number seed: description: Seed number (optional). If not specified, the seed is set to 0. type: integer ExperimentalImpulseSpecificGpuTrainingProcessor: type: string description: > EXPERIMENTAL: Training processor for the impulse-specific-gpu rollout. This field is currently gated by a project experiment and may change in future API versions. enum: - cpu - gpu DspPerformance: type: object required: - latency - ram properties: latency: type: integer ram: type: integer customDspString: type: string description: >- If the project latencyDevice has custom DSP hardware, this value contains a device specific latency metric (eg. cycles) DSPNormalizeData: type: string enum: - none - normalize-channel-standard-scaler KerasVisualLayerType: type: string enum: - dense - conv1d - conv2d - reshape - flatten - dropout - batchNormalization - transfer_mobilenetv2_a35 - transfer_mobilenetv2_a1 - transfer_mobilenetv2_a05 - transfer_mobilenetv2_160_a1 - transfer_mobilenetv2_160_a75 - transfer_mobilenetv2_160_a5 - transfer_mobilenetv2_160_a35 - transfer_mobilenetv1_a25_d100 - transfer_mobilenetv1_a2_d100 - transfer_mobilenetv1_a1_d100 - transfer_kws_mobilenetv1_a1_d100 - transfer_kws_mobilenetv2_a35_d100 - transfer_kws_syntiant_ndp10x - transfer_kws_conv2d_tiny - object_ssd_mobilenet_v2_fpnlite_320x320 - fomo_mobilenet_v2_a01 - fomo_mobilenet_v2_a35 - transfer_organization - transfer_akidanet_imagenet_160_a100 - transfer_akidanet_imagenet_160_a50 - transfer_akidanet_imagenet_160_a25 - transfer_akidanet_imagenet_224_a100 - transfer_akidanet_imagenet_224_a50 - transfer_akidanet_imagenet_224_a25 - fomo_akidanet_a50 - fomo_ad_gmm - fomo_ad_patchcore BlockType: type: string enum: - official - personal - enterprise - community BlockDisplayCategory: description: Category to display this block in the UI. type: string enum: - classical - tao - developer-preview BlockParamsVisualAnomalyPatchcore: type: object properties: backbone: type: string description: The backbone to use for feature extraction numLayers: type: integer description: The number of layers in the feature extractor (1-3) poolSize: type: integer description: The pool size for the feature extractor samplingRatio: type: number description: The sampling ratio for the coreset, used for anomaly scoring numNearestNeighbors: type: integer description: >- The number of nearest neighbors to consider, used for anomaly scoring BlockParamsVisualAnomalyGmm: type: object properties: backbone: type: string description: The backbone to use for feature extraction KerasCustomMetric: type: object required: - name - value properties: name: description: The name of the metric type: string value: description: The value of this metric for this model type type: string ModelPrediction: type: object required: - sampleId - startMs - endMs - prediction properties: sampleId: type: integer startMs: type: number endMs: type: number label: type: string prediction: type: string predictionCorrect: type: boolean f1Score: type: number description: Only set for object detection projects anomalyScores: type: array description: >- 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. items: type: array items: type: number boundingBoxes: type: array description: >- Only set for object detection projects. Coordinates are scaled 0..1, not absolute values. items: $ref: '#/components/schemas/BoundingBoxWithScore' labelMapPredictions: type: object description: >- For samples with structured labels (in the form of a key/value label map), this object will contain per-key prediction info for the sample. additionalProperties: type: string AdditionalMetric: type: object required: - name - value - fullPrecisionValue properties: name: type: string value: type: string fullPrecisionValue: type: number tooltipText: type: string link: type: string KerasModelMetadataGraph: type: object required: - title - data properties: title: description: Graph title type: string xLabel: description: X-axis title type: string yLabel: description: Y-axis title type: string description: description: A description for the graph type: string hideInUI: description: Whether this graph should be hidden by default in the Studio UI type: boolean data: type: array items: $ref: '#/components/schemas/KerasModelMetadataGraphSeries' BoundingBoxWithScore: type: object description: This has the _ratio_ for x/y/w/h (so 0..1) required: - label - x - 'y' - width - height - score properties: label: type: string x: type: number 'y': type: number width: type: number height: type: number score: type: number KerasModelMetadataGraphSeries: type: object required: - title - values properties: title: type: string values: type: array items: type: number securitySchemes: ApiKeyAuthentication: type: apiKey in: header name: x-api-key JWTAuthentication: type: apiKey in: cookie name: jwt JWTHttpHeaderAuthentication: type: apiKey in: header name: x-jwt-token OAuth2: type: oauth2 flows: authorizationCode: authorizationUrl: /v1/oauth/authorize tokenUrl: /v1/oauth/token scopes: openid: Access to basic profile information email: Access to email address profile: Access to full profile information implicit: authorizationUrl: /v1/oauth/authorize scopes: openid: Access to basic profile information email: Access to email address profile: Access to full profile information password: tokenUrl: /v1/oauth/token scopes: openid: Access to basic profile information email: Access to email address profile: Access to full profile information clientCredentials: tokenUrl: /v1/oauth/token scopes: openid: Access to basic profile information email: Access to email address profile: Access to full profile information ````