JSON Prediction Responses

26 Apr 2024
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JSON Prediction Responses

Updated on 26 Apr 2024
9 Minutes to read

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When you run inference on images, the prediction results display in a JSON format. The JSON schema varies based on model type. This article describes the JSON responses for each model type, and provides example code snippets and images.

The JSON schema is slightly different between inferences run via Cloud Deployment, and inferences run via LandingEdge and Docker. This article focuses on the JSON schema used for Cloud Deployment, and notes differences for LandingEdge and Docker when applicable.

JSON Responses for Object Detection Models

The following table describes the objects in the JSON response for Object Detection models. An example response is here.

Element	Description
`backbonetype`	The name of the prediction type. For Object Detection, this will always be `ObjectDetectionPrediction`.
`backbonepredictions`	This object contains the information for each region that the model predicted as an object of interest. Each prediction is a separate object nested in the `backbonepredictions` object.
`xxxxxxxxxxxxx-xxxx-xxxx-xxxxxxxxxxxx`	This alphanumeric number is an internal identifier for the region that the model predicted as an object of interest.
`score`	The confidence score for the prediction.
`labelName`	The predicted class.
`labelIndex`	The internal identifier of the predicted class. Each class in a project is assigned a number, starting with 0. If you delete a class, that class’s number is not re-assigned to any current or future classes in that project.
`coordinates`	The x and y coordinates (in pixels) of the edges of the bounding box around the predicted object of interest.
`defect_id`	The unique identifier for the predicted class.
`predictions`	When an image is sent for inference via Cloud Deployment, it goes through the model, and then through a post-processing Classification step. The `predictions` object contains the results from the Classification step. This step classifies the image as `"OK"` or `"NG"` (short for "Not Good"). This step simplifies defect detection. For example, if a model checks sheet metal for scratches, the metal should be marked as defective regardless if there are one or many scratches. `score`: Confidence score for the Classification prediction. `labelName`: If the number of predictions is > 0 then `"NG"`, else `"OK"`. Inference run via LandingEdge and Docker does not include the post-processing Classification step.
`"type": "ClassificationPrediction"`	When an image is sent for inference via Cloud Deployment, it goes through the model, and then through a post-processing Classification step. This object is the prediction type of the post-processing Classification step.
`latency`	The `latency` object includes the detailed timing statistics of the inference call. Each key-value pair in the `latency` object represents a step in the inference process, and the duration of that step. All values are measured in seconds.
`model_id`	The unique identifier for the model. For more information, go here.

Coordinates in Object Detection Responses

The JSON response for Object Detection predictions includes a coordinates object, which communicates the size and location of the bounding box around the prediction region of interest.

Each section includes the coordinates of the pixels for each edge of the bounding box. The origin for the coordinates is the top left corner of the image.

For example, let's say that the following snippet is the coordinate object in the JSON output for a prediction.

"coordinates": {
   "xmin": 3627,
   "ymin": 979,
   "xmax": 3965,
   "ymax": 1299
},

The coordinates correspond to the points described in the following table and image.

Name	Description	Coordinate (in pixels)
xmin	The distance from the left edge of the image to the left side of the bounding box.	3627
ymin	The distance from the top of the image to the top side of the bounding box.	979
xmax	The distance from the left edge of the image to the right side of the bounding box.	3965
ymax	The distance from the top of the image to the bottom side of the bounding box.	1299

The Coordinate Correspond to the Edges of the Bounding Box of the Prediction

Example: Object Detection JSON Response

The following code snippet and image show the predictions for an Object Detection model trained to detect hard hats. The model has one class: Hard Hat. The model correctly predicted two hard hats. Each prediction is nested in the backbonepredictions object.

{
    "backbonetype": "ObjectDetectionPrediction",
    "backbonepredictions": {
        "4eb97aa5-f2ce-4070-ab46-b2963cb8760f": {
            "score": 0.809603214263916,
            "labelName": "Hard Hat",
            "labelIndex": 1,
            "coordinates": {
                "xmin": 1487,
                "ymin": 1813,
                "xmax": 2103,
                "ymax": 2199
            },
            "defect_id": 152450
        },
        "ae80073f-4f30-48ca-9049-cd227ad210b1": {
            "score": 0.7299894094467163,
            "labelName": "Hard Hat",
            "labelIndex": 1,
            "coordinates": {
                "xmin": 6373,
                "ymin": 1866,
                "xmax": 6955,
                "ymax": 2195
            },
            "defect_id": 152450
        }
    },
    "predictions": {
        "score": 0.809603214263916,
        "labelName": "NG",
        "labelIndex": 1
    },
    "type": "ClassificationPrediction",
    "latency": {
        "preprocess_s": 0.4117429256439209,
        "infer_s": 0.329150915145874,
        "postprocess_s": 0.0005364418029785156,
        "serialize_s": 0.00045990943908691406,
        "input_conversion_s": 0.639418363571167,
        "model_loading_s": 5.3452112674713135
    },
    "model_id": "832050ab-dd69-46a2-84a9-5a811f2e8188"
}

The Overlay Corresponds to the Prediction Data in the JSON Response for the Object Detection Model

JSON Responses for Segmentation and Visual Prompting Models

The following table describes the objects in the JSON response for Segmentation and Visual Prompting models. See these Segmentation and Visual Prompting examples.

Element	Description
`backbonetype`	The name of the prediction type. For Segmentation, this will always be `SegmentationPrediction`. For Visual Prompting, this will always be `null`.
`backbonepredictions`	For Segmentation, this object contains information about the image and the model's predictions. For Visual Prompting, this will always be `null`.
`imageHeight`	The height of the image in pixels.
`imageWidth`	The width of the image in pixels.
`encoding`	The information about how LandingLens encodes Segmentation predictions. Predictions are encoded with run-length encoding (RLE). 0 is mapped to Z, and 1 is mapped to N. This section will always be: `"encoding": { "algorithm": "rle", "options": { "map": { "Z": 0, "N": 1 } } },`
`bitmaps`	This object contains the information for each class that the model predicted in the image. Each predicted class is a separate object nested in the `bitmaps` object. LandingEdge includes data for all classes in the project, and not just the ones predicted in the image.
`xxxxxxxxxxxxx-xxxx-xxxx-xxxxxxxxxxxx`	This alphanumeric number is an internal identifier for the region that the model predicted as an object of interest. The prediction information is nested in this object.
`score`	The confidence score for the prediction.
`bitmap`	The prediction mask (the areas that the model predicted for a specific class) is formatted as a bitmap that is compressed with a run-length encoding (RLE) format. The Landing AI Python library parses the JSON responses, which includes parsing and extracting bitmaps. We recommend using the library if you want to avoid manually parsing the bitmaps. Check out the Satellite Images and Post-Processing tutorial for an end-to-end example of running inference and visualizing the results with our Python library. You can use the decode_bitmap_rle API to decode the bitmap string into a NumPy array, which you can then convert into a PNG file.
`defect_id`	The unique identifier for the predicted class.
`labelIndex`	The internal identifier of the predicted class. Each class in a project is assigned a number, starting with 0. If you delete a class, that class’s number is not re-assigned to any current or future classes in that project.
`labelName`	The predicted class.
`data`	This will always be `null`. This object is not included in responses from LandingEdge and Docker.
`labels`	This will always be `null`. This object is not included in responses from LandingEdge and Docker.
`num_classes`	This is the total number of classes in the project. Segmentation and Visual Prompting projects have an internal class called `“ok”`. This class is included in the `num_class` object. So if you created 3 classes in the project, the `num_class` value is 4. This object is not included in responses from LandingEdge and Docker.
`predictions`	When an image is sent for inference via Segmentation or Visual Prompting, it goes through the model, and then through a post-processing Classification step. The `predictions` object contains the results from the Classification step. This step classifies the image as `"OK"` or `"NG"` (short for "Not Good"). This step simplifies defect detection. For example, if a model checks sheet metal for scratches, the metal should be marked as defective regardless if there are one or many scratches. `score`: Confidence score for the Classification prediction. `labelName`: If the number of predictions is > 0 then `"NG"`, else `"OK"`. Inference run via LandingEdge and Docker does not include the post-processing Classification step.
`"type": "ClassificationPrediction"`	When an image is sent for inference via Cloud Deployment, it goes through the model, and then through a post-processing Classification step. This object is the prediction type of the post-processing Classification step.
`latency`	The `latency` object includes the detailed timing statistics of the inference call. Each key-value pair in the `latency` object represents a step in the inference process, and the duration of that step. All values are measured in seconds.
`model_id`	The unique identifier for the model. For more information, go here.

Example: Segmentation JSON Response

The following code snippet and image show the predictions for a Segmentation model trained to detect chips on pills. The model has one class: Chipped. Even though there is more than one chipped region, all of the data for Chipped predictions are included in one bitmap object, because these regions all correspond to the Chipped class.

In the image, the purple overlay is the prediction for Chipped.

{
    "backbonetype": "SegmentationPrediction",
    "backbonepredictions": {
        "imageHeight": 1024,
        "imageWidth": 1024,
        "encoding": {
            "algorithm": "rle",
            "options": {
                "map": {
                    "Z": 0,
                    "N": 1
                }
            }
        },
        "bitmaps": {
            "8276bc82-d378-3ea0-ca4e-51c0549f73db": {
                "score": 0.969035930985445,
                "bitmap": "315776Z2N23Z13N984Z8N16Z21N978Z10N14Z23N977Z10N14Z23N975Z12N13Z25N973Z14N9Z30N954Z8N7Z16N7Z34N949Z35N3Z40N2Z2N942Z35N3Z40N2Z2N938Z88N934Z91N931Z100N923Z102N922Z102N922Z103N918Z108N912Z112N912Z112N908Z117N905Z119N902Z123N901Z124N900Z124N899Z127N897Z128N895Z130N894Z130N892Z134N890Z135N888Z136N887Z138N886Z138N885Z139N885Z139N883Z142N882Z142N881Z143N881Z143N880Z144N880Z144N880Z144N878Z146N878Z146N877Z146N878Z146N878Z146N877Z147N877Z147N877Z147N877Z147N877Z146N878Z146N878Z146N878Z146N878Z146N877Z147N877Z147N877Z146N878Z146N878Z146N878Z146N879Z145N880Z142N882Z142N882Z142N883Z141N885Z139N885Z139N885Z139N886Z138N886Z138N886Z138N886Z138N886Z140N884Z140N884Z141N883Z141N883Z141N884Z140N884Z141N885Z139N885Z139N885Z140N884Z140N885Z141N883Z141N883Z141N883Z141N883Z139N885Z138N886Z138N886Z137N887Z134N891Z126N898Z119N905Z119N905Z117N907Z117N907Z117N907Z117N907Z117N908Z116N908Z116N910Z114N910Z114N910Z114N910Z114N911Z113N911Z113N911Z113N912Z112N912Z110N916Z108N916Z108N916Z108N916Z108N917Z107N917Z107N917Z107N917Z107N917Z107N917Z107N917Z107N917Z107N916Z110N914Z110N912Z112N912Z112N911Z113N911Z114N909Z116N908Z116N906Z119N905Z121N902Z122N902Z123N901Z123N901Z123N900Z124N900Z125N899Z125N899Z125N899Z125N899Z127N898Z126N898Z126N898Z126N898Z127N897Z127N11Z4N882Z127N10Z4N883Z127N10Z4N883Z127N10Z3N883Z128N8Z5N883Z128N6Z7N883Z128N6Z7N883Z141N883Z139N884Z140N884Z140N884Z140N885Z136N888Z107N9Z16N892Z104N920Z104N920Z101N924Z96N928Z90N934Z80N944Z80N944Z76N948Z76N948Z76N5Z4N938Z78N1Z10N935Z78N1Z10N935Z91N130Z3N800Z89N132Z4N798Z90N132Z5N797Z90N132Z5N797Z90N132Z7N795Z89N132Z8N795Z88N133Z9N794Z88N132Z10N794Z88N132Z10N794Z87N131Z12N794Z84N134Z12N794Z83N134Z13N794Z83N134Z13N795Z55N4Z22N134Z14N795Z51N10Z17N136Z15N795Z47N16Z14N137Z15N796Z45N18Z10N113Z9N7Z7N4Z15N796Z45N18Z10N113Z9N7Z7N4Z15N796Z45N20Z4N114Z26N2Z16N797Z44N138Z28N1Z16N797Z42N139Z29N1Z16N797Z42N139Z29N1Z16N797Z41N140Z46N797Z40N139Z46N799Z37N132Z56N799Z36N132Z57N799Z36N132Z57N800Z34N133Z57N802Z30N134Z58N803Z27N136Z58N806Z21N139Z57N807Z21N139Z57N808Z16N141Z59N810Z12N143Z59N812Z9N144Z59N812Z9N144Z59N813Z5N147Z59N814Z3N147Z61N962Z62N960Z66N958Z66N957Z68N8Z7N935Z93N929Z98N5Z8N913Z98N5Z8N913Z112N912Z113N910Z114N910Z115N909Z115N908Z118N906Z119N904Z123N14Z5N882Z123N14Z5N882Z126N9Z9N878Z131N5Z12N875Z134N3Z13N873Z152N872Z152N871Z154N868Z156N867Z157N866Z158N866Z158N863Z163N860Z164N859Z165N859Z165N859Z166N856Z169N855Z174N850Z177N847Z177N847Z179N844Z182N842Z183N841Z183N840Z184N838Z186N838Z187N835Z189N835Z189N830Z194N814Z7N7Z198N811Z9N5Z199N811Z9N5Z199N810Z11N4Z199N809Z14N2Z200N808Z14N2Z202N806Z221N803Z221N803Z222N802Z221N803Z218N807Z214N810Z214N810Z211N813Z211N812Z213N811Z213N805Z219N803Z221N799Z227N794Z230N794Z230N791Z233N786Z12N7Z219N783Z14N8Z219N783Z14N8Z219N783Z17N5Z219N783Z19N1Z221N782Z242N782Z242N782Z242N782Z242N783Z241N783Z241N783Z241N769Z255N769Z255N769Z255N769Z256N768Z256N768Z256N768Z256N767Z258N766Z258N766Z258N766Z258N766Z258N766Z258N766Z258N766Z258N764Z260N764Z260N763Z261N763Z261N763Z260N755Z1N7Z261N763Z261N763Z261N763Z260N762Z262N762Z260N763Z260N764Z260N763Z260N764Z258N766Z258N766Z258N765Z259N763Z261N763Z261N763Z260N764Z260N763Z261N763Z261N763Z261N763Z261N763Z261N763Z260N764Z260N765Z259N765Z259N765Z258N766Z258N768Z256N769Z253N771Z253N771Z253N770Z253N771Z253N771Z253N769Z255N769Z255N769Z254N770Z254N770Z254N772Z252N772Z252N772Z251N773Z251N773Z251N773Z251N773Z249N776Z247N777Z247N777Z246N778Z244N781Z242N782Z240N784Z240N784Z237N787Z236N789Z233N791Z233N791Z231N793Z230N794Z228N798Z224N800Z224N800Z222N803Z219N806Z217N807Z217N809Z213N812Z105N2Z103N815Z99N9Z98N818Z95N15Z95N819Z95N15Z95N820Z93N18Z93N822Z89N21Z91N824Z88N21Z91N824Z88N21Z91N825Z86N23Z62N2Z24N829Z84N24Z61N2Z23N831Z82N27Z57N5Z21N832Z80N29Z56N6Z20N833Z80N29Z56N6Z20N834Z79N30Z55N8Z16N837Z77N31Z54N9Z16N837Z76N33Z53N10Z14N838Z63N4Z8N35Z50N13Z12N839Z63N4Z8N35Z50N13Z12N841Z60N50Z43N19Z9N843Z59N53Z11N2Z27N24Z4N844Z59N60Z1N12Z16N876Z59N60Z1N12Z16N877Z56N968Z22N6Z27N970Z18N16Z19N973Z15N19Z15N975Z15N19Z15N976Z11N23Z7N984Z9N26Z2N988Z8N1016Z8N1019Z5N1020Z4N1021Z2N309854Z",
                "defect_id": 62504,
                "label_index": 1,
                "label_name": "Chipped"
            }
        },
        "data": null,
        "labels": null,
        "num_classes": 2
    },
    "predictions": {
        "score": 0.9999961853027344,
        "labelName": "NG",
        "labelIndex": 1
    },
    "type": "ClassificationPrediction",
    "latency": {
        "preprocess_s": 0.003261089324951172,
        "infer_s": 2.586284875869751,
        "postprocess_s": 0.0012137889862060547,
        "serialize_s": 0.029936552047729492,
        "input_conversion_s": 0.034484148025512695,
        "model_loading_s": 7.019518852233887
    },
    "model_id": "d35f3269-ebe9-4f0a-8694-1f65c5ee7036"
}

The Purple Overlay Corresponds to the Prediction Data in the JSON Response for the Segmentation Model

Example: Visual Prompting JSON Response

The following code snippet and image show the predictions for a Visual Prompting model trained to detect shipping containers in ports. The model has two classes: Shipping Containers and Other. Each class is a separate object nested in the bitmaps object.

In the image, the purple overlay is the prediction for Shipping Containers, and the yellow overlay is the prediction for Other.

{
    "backbonetype": null,
    "backbonepredictions": null,
    "predictions": {
        "imageHeight": 563,
        "imageWidth": 1000,
        "encoding": {
            "algorithm": "rle",
            "options": {
                "map": {
                    "Z": 0,
                    "N": 1
                }
            }
        },
        "bitmaps": {
            "d7d654b7-01ac-e5c4-af5b-3a5d28c6cb20": {
                "score": 1.0,
                "bitmap": 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                "label_index": 2,
                "label_name": "Other"
            }
        },
        "data": null,
        "labels": null,
        "num_classes": 3
    },
    "type": "SegmentationPrediction",
    "latency": {
        "preprocess_s": 0.0034046173095703125,
        "infer_s": 0.6407222747802734,
        "postprocess_s": 7.05718994140625e-05,
        "serialize_s": 0.05350923538208008,
        "input_conversion_s": 0.01775670051574707,
        "model_loading_s": 8.249282836914062e-05
    },
    "model_id": "fdb43e8d-c316-4e16-a7ae-08d7454a83de"
}

The Purple and Yellow Overlays Correspond to the Prediction Data in the JSON Response for the Visual Prompting Model

JSON Responses for Classification Models

The following table describes the objects in the JSON response for Classification models. An example response is here.

Element	Description
`backbonetype`	This object isn’t applicable to Classification models, because the prediction type is captured in the `type` object. For Classification, this will always be `null`.
`backbonepredictions`	This object isn’t applicable to Classification models, because the prediction type is captured in predictions. For Classification, this will always be `null`.
`predictions`	This object contains the information for the model's prediction.
`score`	The confidence score for the prediction.
`labelName`	The predicted class.
`labelIndex`	The internal identifier of the predicted class. Each class in a project is assigned a number, starting with 0. If you delete a class, that class’s number is not re-assigned to any current or future classes in that project.
`"type": "ClassificationPrediction"`	The name of the prediction type. For Classification, this will always be ClassificationPrediction.
`latency`	The `latency` object includes the detailed timing statistics of the inference call. Each key-value pair in the `latency` object represents a step in the inference process, and the duration of that step. All values are measured in seconds.
`model_id`	The unique identifier for the model. For more information, go here.

Example: Classification JSON Response

The following code snippet and image show the predictions for a Classification model trained to detect defects on metal sheets. The model has three classes: Pitted, Scratched, and No Defect. The model correctly predicted the Scratched class.

The prediction data is in the predictions object.

{
    "backbonetype": null,
    "backbonepredictions": null,
    "predictions": {
        "score": 0.9992166757583618,
        "labelName": "Scratched",
        "labelIndex": 1
    },
    "type": "ClassificationPrediction",
    "latency": {
        "preprocess_s": 0.005140542984008789,
        "infer_s": 0.14725708961486816,
        "postprocess_s": 6.794929504394531e-05,
        "serialize_s": 0.00016546249389648438,
        "input_conversion_s": 0.0007550716400146484,
        "model_loading_s": 5.677931785583496
    },
    "model_id": "6924128f-3526-4d02-97c5-281f8a6984c1"
}

The Overlay Corresponds to the Prediction Data in the JSON Response for the Classification Model

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Cloud Inference (Classic Flow)

Table of contents

JSON Responses for Object Detection Models
JSON Responses for Segmentation and Visual Prompting Models
JSON Responses for Classification Models