> ## 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. # Test pretrained model using image data > Test out a pretrained model (using image data) - upload first via `uploadPretrainedModel`. If you want to deploy a pretrained model from the API, see `startDeployPretrainedModelJob`. This will transform raw image data (e.g. RGB to grayscale, resize) before classifying. To classify raw features, see `testPretrainedModel`. ## OpenAPI ````yaml /.assets/openapi.yaml post /api/{projectId}/pretrained-model/test-image 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}/pretrained-model/test-image: post: tags: - Learn summary: Test pretrained model using image data description: > Test out a pretrained model (using image data) - upload first via `uploadPretrainedModel`. If you want to deploy a pretrained model from the API, see `startDeployPretrainedModelJob`. This will transform raw image data (e.g. RGB to grayscale, resize) before classifying. To classify raw features, see `testPretrainedModel`. operationId: testPretrainedModelImages parameters: - $ref: '#/components/parameters/ProjectIdParameter' - $ref: '#/components/parameters/OptionalImpulseIdParameter' requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/TestPretrainedModelImagesRequest' responses: '200': description: OK content: application/json: schema: $ref: '#/components/schemas/TestPretrainedModelResponse' components: parameters: ProjectIdParameter: name: projectId in: path required: true description: Project ID schema: type: integer OptionalImpulseIdParameter: name: impulseId in: query required: false description: Impulse ID. If this is unset then the default impulse is used. schema: type: integer schemas: TestPretrainedModelImagesRequest: type: object required: - imageFileBase64 - input - model properties: imageFileBase64: description: A base64 encoded input image file type: string input: type: object required: - resizeMode - inputScaling properties: resizeMode: $ref: '#/components/schemas/ImageInputResizeMode' inputScaling: $ref: '#/components/schemas/ImageInputScaling' model: discriminator: propertyName: modelType mapping: classification: $ref: '#/components/schemas/DeployPretrainedModelModelClassification' regression: $ref: '#/components/schemas/DeployPretrainedModelModelRegression' object-detection: $ref: '#/components/schemas/DeployPretrainedModelModelObjectDetection' freeform: $ref: '#/components/schemas/DeployPretrainedModelModelFreeform' anomaly: $ref: '#/components/schemas/DeployPretrainedModelModelAnomaly' visual-anomaly: $ref: '#/components/schemas/DeployPretrainedModelModelVisualAnomaly' oneOf: - $ref: '#/components/schemas/DeployPretrainedModelModelClassification' - $ref: '#/components/schemas/DeployPretrainedModelModelRegression' - $ref: '#/components/schemas/DeployPretrainedModelModelObjectDetection' - $ref: '#/components/schemas/DeployPretrainedModelModelFreeform' - $ref: '#/components/schemas/DeployPretrainedModelModelAnomaly' - $ref: '#/components/schemas/DeployPretrainedModelModelVisualAnomaly' TestPretrainedModelResponse: allOf: - $ref: '#/components/schemas/GenericApiResponse' - type: object properties: result: type: object description: >- Classification value per label. For a neural network this will be the confidence, for anomalies the anomaly score. additionalProperties: type: number boundingBoxes: type: array items: $ref: '#/components/schemas/BoundingBoxWithScore' freeformResult: type: object required: - outputTensors properties: outputTensors: type: array items: type: object required: - shape - dataType - data properties: shape: type: array items: type: integer dataType: type: string enum: - int8 - uint8 - float32 data: type: array items: type: number anomalyResult: type: array description: >- Anomaly scores and computed metrics for visual anomaly detection, one item per window. items: $ref: '#/components/schemas/AnomalyResult' 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 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 DeployPretrainedModelModelClassification: type: object required: - modelType - labels properties: modelType: type: string enum: - classification labels: type: array items: type: string DeployPretrainedModelModelRegression: type: object required: - modelType properties: modelType: type: string enum: - regression DeployPretrainedModelModelObjectDetection: type: object required: - modelType - labels - lastLayer - minimumConfidence properties: modelType: type: string enum: - object-detection labels: type: array items: type: string lastLayer: $ref: '#/components/schemas/ObjectDetectionLastLayer' minimumConfidence: description: >- Deprecated: use thresholdValues instead. Threshold for objects (f.e. 0.3). type: number thresholdValues: type: object description: >- All configured thresholds for the current model. Valid keys are 'min_score' (object detection models, all but paddleocr-detector); 'min_score_pixel', 'min_score_box', 'unclip_ratio' (paddleocr-detector). additionalProperties: type: number DeployPretrainedModelModelFreeform: type: object required: - modelType properties: modelType: type: string enum: - freeform DeployPretrainedModelModelAnomaly: type: object required: - modelType properties: modelType: type: string enum: - anomaly DeployPretrainedModelModelVisualAnomaly: type: object required: - modelType properties: modelType: type: string enum: - visual-anomaly thresholdValues: type: object description: >- All configured thresholds for the current model. Valid keys are 'min_anomaly_score' and 'anomaly_scoring_aggregation_method'. additionalProperties: $ref: '#/components/schemas/ThresholdValue' 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) 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 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. 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 ThresholdValue: description: Current value of the threshold oneOf: - type: number - type: string 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 ````