> ## 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. # Classify sample by learn block > 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. ## OpenAPI ````yaml /.assets/openapi.yaml post /api/{projectId}/classify/anomaly-gmm/v2/{blockId}/{sampleId} 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}/classify/anomaly-gmm/v2/{blockId}/{sampleId}: post: tags: - Classify summary: Classify sample by learn block description: > 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. operationId: classifySampleByLearnBlockV2 parameters: - $ref: '#/components/parameters/ProjectIdParameter' - $ref: '#/components/parameters/SampleIdParameter' - $ref: '#/components/parameters/BlockIdParameter' - $ref: '#/components/parameters/ModelVariantParameter' - $ref: '#/components/parameters/TruncateStructuredLabelsParameter' responses: '200': description: OK content: application/json: schema: anyOf: - $ref: '#/components/schemas/ClassifySampleResponse' - $ref: '#/components/schemas/StartJobResponse' components: parameters: ProjectIdParameter: name: projectId in: path required: true description: Project ID schema: type: integer SampleIdParameter: name: sampleId in: path required: true description: Sample ID schema: type: integer BlockIdParameter: name: blockId in: path required: true description: Block ID schema: type: integer ModelVariantParameter: name: variant in: query required: false description: Keras model variant schema: $ref: '#/components/schemas/KerasModelVariantEnum' TruncateStructuredLabelsParameter: name: truncateStructuredLabels in: query required: false description: >- If true, only a slice of labels will be returned for samples with multiple labels. schema: type: boolean schemas: ClassifySampleResponse: allOf: - $ref: '#/components/schemas/GenericApiResponse' - type: object required: - classifications - sample - windowSizeMs - windowIncreaseMs - alreadyInDatabase properties: classifications: type: array items: $ref: '#/components/schemas/ClassifySampleResponseClassification' sample: $ref: '#/components/schemas/RawSampleDataWithDataStreamPayloads' windowSizeMs: type: integer description: >- Size of the sliding window (as set by the impulse) in milliseconds. example: 2996 windowIncreaseMs: type: integer description: >- Number of milliseconds that the sliding window increased with (as set by the impulse) example: 10 alreadyInDatabase: type: boolean description: Whether this sample is already in the training database warning: type: string StartJobResponse: allOf: - $ref: '#/components/schemas/GenericApiResponse' - type: object required: - id properties: id: type: integer description: Job identifier. Status updates will include this identifier. example: 12873488112 KerasModelVariantEnum: type: string enum: - int8 - float32 - akida 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) ClassifySampleResponseClassification: type: object required: - learnBlock - result - expectedLabels - minimumConfidenceRating - thresholds - intervalMs - frequencyHz properties: learnBlock: $ref: '#/components/schemas/ImpulseLearnBlock' result: type: array description: Classification result, one item per window. example: - idle: 0.0002 wave: 0.9998 anomaly: -0.42 items: type: object description: >- Classification value per label. For a neural network this will be the confidence, for anomalies the anomaly score. additionalProperties: type: number anomalyResult: type: array description: >- Anomaly scores and computed metrics for visual anomaly detection, one item per window. items: $ref: '#/components/schemas/AnomalyResult' structuredResult: type: array description: >- Results of inferencing that returns structured data, such as object detection items: $ref: '#/components/schemas/StructuredClassifyResult' minimumConfidenceRating: type: number description: >- DEPRECATED, see "thresholds" instead. The minimum confidence rating for this block. For regression, this is the absolute error (which can be larger than 1). details: type: array description: >- Structured outputs and computed metrics for some model types (e.g. object detection), one item per window. items: $ref: '#/components/schemas/ClassifySampleResponseClassificationDetails' objectDetectionLastLayer: $ref: '#/components/schemas/ObjectDetectionLastLayer' expectedLabels: type: array description: An array with an expected label per window. items: $ref: '#/components/schemas/StructuredLabel' expectedAnomalyOutcome: type: array description: > An array with the expected anomaly outcome for each window — either “anomaly” or “no anomaly”. The outcome is determined by which labels are marked as anomalous in the project setup, or by the sample label if no such configuration is defined. items: $ref: '#/components/schemas/StructuredLabel' thresholds: type: array description: List of configurable thresholds for this block. items: $ref: '#/components/schemas/BlockThreshold' isMultiLabel: type: boolean intervalMs: description: Interval between two windows in "expectedLabels" (1000 / frequency). type: number frequencyHz: description: Frequency of the windows in "expectedLabels" type: number RawSampleDataWithDataStreamPayloads: allOf: - $ref: '#/components/schemas/RawSampleData' - $ref: '#/components/schemas/RawSampleDataStreamPayloads' 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' AnomalyResult: type: object properties: boxes: type: array description: >- 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. items: $ref: '#/components/schemas/BoundingBoxWithScore' scores: type: array description: >- 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. items: type: array items: type: number meanScore: type: number description: Mean value of the scores. maxScore: type: number description: Maximum value of the scores. StructuredClassifyResult: type: object required: - boxes - scores - mAP - f1 - precision - recall properties: boxes: type: array description: >- For object detection. An array of bounding box arrays, (x, y, width, height), one per detection in the image. items: type: array items: type: number labels: type: array description: >- For object detection. An array of labels, one per detection in the image. items: type: string scores: type: array description: >- For object detection. An array of probability scores, one per detection in the image. items: type: number mAP: type: number description: >- For object detection. A score that indicates accuracy compared to the ground truth, if available. f1: type: number description: >- For FOMO. A score that combines the precision and recall of a classifier into a single metric, if available. precision: type: number description: >- A measure of how many of the positive predictions made are correct (true positives). recall: type: number description: >- A measure of how many of the positive cases the classifier correctly predicted, over all the positive cases. debugInfoJson: type: string description: Debug info in JSON format example: | { "y_trues": [ {"x": 0.854, "y": 0.453125, "label": 1}, {"x": 0.197, "y": 0.53125, "label": 2} ], "y_preds": [ {"x": 0.916, "y": 0.875, "label": 1}, {"x": 0.25, "y": 0.541, "label": 2} ], "assignments": [ {"yp": 1, "yt": 1, "label": 2, "distance": 0.053} ], "normalised_min_distance": 0.2, "all_pairwise_distances": [ [0, 0, 0.426], [1, 1, 0.053] ], "unassigned_y_true_idxs": [0], "unassigned_y_pred_idxs": [0] } ClassifySampleResponseClassificationDetails: type: object properties: boxes: type: array description: Bounding boxes predicted by localization model items: type: array items: type: number labels: type: array description: Labels predicted by localization model items: type: number scores: type: array description: Scores predicted by localization model items: type: number mAP: type: number description: >- For object detection, the COCO mAP computed for the predictions on this image f1: type: number description: For FOMO, the F1 score computed for the predictions on this image 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 StructuredLabel: type: object description: >- A structured label contains a label, and the range for which this label is valid. `endIndex` is inclusive. E.g. `{ startIndex: 10, endIndex: 13, label: 'running' }` means that the values at index 10, 11, 12, 13 are labeled 'running'. To get time codes you can multiple by the sample's `intervalMs` property. required: - startIndex - endIndex - label properties: startIndex: type: integer description: Start index of the label (e.g. 0) endIndex: type: integer description: >- End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3. label: type: string description: The label for this section. labelMap: $ref: '#/components/schemas/SampleLabelMapLabels' 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" RawSampleData: type: object required: - sample - payload - totalPayloadLength properties: sample: $ref: '#/components/schemas/Sample' payload: $ref: '#/components/schemas/RawSamplePayload' description: >- If multiple payloads are present (one per datastream) this returns the first datastream. totalPayloadLength: type: integer description: Total number of payload values RawSampleDataStreamPayloads: type: object required: - datastreamPayloads properties: datastreamPayloads: type: array items: type: object required: - index - payload - totalPayloadLength properties: index: type: integer description: >- Index of the datastream in the sample (datastreams that are not in the impulse are filtered out here, so you can still find them back). payload: $ref: '#/components/schemas/RawSamplePayload' description: Payload for the current datastream. totalPayloadLength: type: integer description: Total number of payload values 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 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 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 SampleLabelMapLabels: description: > Structured sample labels in the form of a key-value map. This property is optional and only defined for samples with key-value labels. discriminator: propertyName: type mapping: key-values: $ref: '#/components/schemas/SampleKeyValueLabels' oneOf: - $ref: '#/components/schemas/SampleKeyValueLabels' Sample: type: object required: - id - filename - signatureValidate - created - lastModified - category - coldstorageFilename - label - intervalMs - frequency - originalIntervalMs - originalFrequency - deviceType - sensors - valuesCount - added - boundingBoxes - boundingBoxesType - chartType - isDisabled - isProcessing - processingError - isCropped - projectId - sha256Hash - datastreams properties: id: type: integer example: 2 filename: type: string example: idle01.d8Ae signatureValidate: type: boolean description: Whether signature validation passed example: true signatureMethod: type: string example: HS256 signatureKey: type: string description: >- Either the shared key or the public key that was used to validate the sample created: type: string format: date-time description: >- 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. lastModified: type: string format: date-time description: Timestamp when the sample was last modified. category: $ref: '#/components/schemas/RawDataCategory' example: training coldstorageFilename: type: string label: type: string example: healthy-machine intervalMs: type: number description: >- Interval between two windows (1000 / frequency). NOTE: Previously this value could change depending on whether the sample was resampled. This behavior has been removed. example: 16 frequency: type: number description: >- Frequency of the sample. NOTE: Previously this value could change depending on whether the sample was resampled. This behavior has been removed. example: 62.5 originalIntervalMs: type: number description: DEPRECATED. See "intervalMs". example: 16 originalFrequency: type: number description: DEPRECATED. See "frequency". example: 62.5 deviceName: type: string deviceType: type: string sensors: type: array items: $ref: '#/components/schemas/Sensor' valuesCount: type: integer description: >- Number of readings in this file. If you have multiple datastreams, this is the value count from the datastream with the highest frequency. totalLengthMs: type: number description: Total length (in ms.) of this file added: type: string format: date-time description: >- Timestamp when the sample was added to the current acquisition bucket. boundingBoxes: type: array items: $ref: '#/components/schemas/BoundingBox' boundingBoxesType: type: string enum: - object_detection - constrained_object_detection chartType: type: string enum: - chart - image - video - table thumbnailVideo: type: string thumbnailVideoFull: type: string isDisabled: type: boolean description: True if the current sample is excluded from use isProcessing: type: boolean description: True if the current sample is still processing (e.g. for video) processingJobId: type: integer description: Set when sample is processing and a job has picked up the request processingError: type: boolean description: Set when processing this sample failed processingErrorString: type: string description: Error (only set when processing this sample failed) isCropped: type: boolean description: >- Whether the sample is cropped from another sample (and has crop start / end info) metadata: type: object description: Sample free form associated metadata additionalProperties: type: string projectId: type: integer description: Unique identifier of the project this sample belongs to projectOwnerName: type: string description: Name of the owner of the project this sample belongs to projectName: type: string description: Name of the project this sample belongs to projectLabelingMethod: $ref: '#/components/schemas/ProjectLabelingMethod' description: What labeling flow the project this sample belongs to uses sha256Hash: type: string description: Data sample SHA 256 hash (including CBOR envelope if applicable) structuredLabels: type: array items: $ref: '#/components/schemas/StructuredLabel' structuredLabelsList: type: array items: type: string createdBySyntheticDataJobId: type: integer description: >- If this sample was created by a synthetic data job, it's referenced here. imageDimensions: type: object required: - width - height properties: width: type: integer height: type: integer videoUrl: type: string description: Video link, cropped and in original resolution. videoUrlFull: type: string description: Video link in original resolution. labelMap: $ref: '#/components/schemas/SampleLabelMapLabels' datastreams: type: array items: $ref: '#/components/schemas/SampleDatastream' RawSamplePayload: type: object description: Sensor readings and metadata required: - device_type - sensors - values - intervalMs - frequencyHz properties: device_name: type: string description: >- Unique identifier for this device. **Only** set this when the device has a globally unique identifier (e.g. MAC address). example: ac:87:a3:0a:2d:1b device_type: type: string description: >- Device type, for example the exact model of the device. Should be the same for all similar devices. example: DISCO-L475VG-IOT01A sensors: type: array description: Array with sensor axes items: $ref: '#/components/schemas/Sensor' values: type: array description: > 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. items: type: array items: type: number cropStart: type: integer description: New start index of the cropped sample example: 0 cropEnd: type: integer description: New end index of the cropped sample example: 128 intervalMs: type: number description: >- Interval between two windows (1000 / frequency). If the data was resampled, then this lists the resampled interval for this payload. example: 16 frequencyHz: type: number description: >- Frequency of the sample. If the data was resampled, then this lists the resampled frequency for this payload. example: 62.5 SampleKeyValueLabels: type: object required: - type - labels properties: type: type: string enum: - key-values labels: type: object additionalProperties: type: string RawDataCategory: type: string enum: - training - testing - validation - post-processing Sensor: type: object required: - index - name - units properties: index: type: integer description: >- Index of the sensor (globally, so you can refer back to it in e.g. getSampleAsAudio) name: type: string description: Name of the axis example: accX units: type: string description: >- Type of data on this axis. Needs to comply to SenML units (see https://www.iana.org/assignments/senml/senml.xhtml). BoundingBox: type: object description: >- This has the _absolute values_ for x/y/w/h (so 0..x (where x is the w/h of the image)) required: - label - x - 'y' - width - height properties: label: type: string x: type: integer 'y': type: integer width: type: integer height: type: integer ProjectLabelingMethod: type: string enum: - single_label - object_detection - label_map SampleDatastream: type: object required: - index - chartType - intervalMs - frequencyHz - sensors - valuesCount - totalLengthMs properties: index: type: integer chartType: type: string enum: - chart - image - video - table intervalMs: description: Interval between two windows (1000 / frequency). type: number frequencyHz: description: Frequency of the datastream type: number sensors: type: array items: $ref: '#/components/schemas/Sensor' valuesCount: type: integer description: Number of readings in this datastream. imageDimensions: type: object required: - width - height properties: width: type: integer height: type: integer totalLengthMs: type: number description: Total length (in ms.) of the datastream 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 ````