> ## 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. # Keras metadata > Get metadata about a trained Keras block. Use the impulse blocks to find the learnId. ## OpenAPI ````yaml /.assets/openapi.yaml get /api/{projectId}/training/keras/{learnId}/metadata 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}/training/keras/{learnId}/metadata: get: tags: - Learn summary: Keras metadata description: >- Get metadata about a trained Keras block. Use the impulse blocks to find the learnId. operationId: getKerasMetadata parameters: - $ref: '#/components/parameters/ProjectIdParameter' - $ref: '#/components/parameters/LearnIdParameter' - $ref: '#/components/parameters/ExcludeLabelsParameter' responses: '200': description: OK content: application/json: schema: $ref: '#/components/schemas/KerasModelMetadataResponse' components: parameters: ProjectIdParameter: name: projectId in: path required: true description: Project ID schema: type: integer LearnIdParameter: name: learnId in: path required: true description: Learn Block ID, use the impulse functions to retrieve the ID schema: type: integer ExcludeLabelsParameter: name: excludeLabels in: query required: false description: >- If set to "true", the "labels" field is left empty (which can be big on e.g. regression projects). schema: type: boolean schemas: KerasModelMetadataResponse: allOf: - $ref: '#/components/schemas/GenericApiResponse' - $ref: '#/components/schemas/KerasModelMetadata' 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) 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 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: '' KerasModelTypeEnum: type: string enum: - int8 - float32 - akida - requiresRetrain 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 - qc-yolox 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 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" TensorboardGraphs: type: array items: $ref: '#/components/schemas/KerasModelMetadataGraph' 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 expectedAnomalyOutcome: type: string description: > Only set for anomaly detection projects. The expected anomaly outcome for this 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. 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 ````