mAP Results

Returns the primary metric detail for the evaluation. The response shape depends on the task type:

• Object detection / instance segmentation - mAP at IoU 0.5 / 0.5-0.95 / 0.75 per split, broken down by object size and per class.

• Semantic segmentation - mIoU, precision, recall, F1 (pixel-level) per split, with per-class IoU and optimal confidence thresholds.

The taskType field in the response indicates which shape to expect: "object-detection-like" or "semantic-segmentation".

This is the data the metrics per split panel in the app reads.

https://api.roboflow.com/:workspace/model-evals/:evalId/map-results

curl "https://api.roboflow.com/my-workspace/model-evals/$EVAL_ID/map-results?api_key=$ROBOFLOW_API_KEY"

Response (object detection / instance segmentation)

{
    "taskType": "object-detection-like",
    "splits": {
        "test": {
            "map50": 0.9239650566041828,
            "map50_95": 0.7555258345429926,
            "map75": 0.9239650566041828,
            "byObjectSize": {
                "small": {
                    "map50": 0.9038189533239035,
                    "map50_95": 0.6478143732740621,
                    "map75": 0.9038189533239035
                },
                "medium": {
                    "map50": 0.9913366336633663,
                    "map50_95": 0.8572608399609195,
                    "map75": 0.9913366336633663
                },
                "large": null
            },
            "perClass": {
                "Car-rims": {
                    "map50": 0.9239650566041828,
                    "map50_95": 0.7555258345429926,
                    "map75": 0.9239650566041828,
                    "byObjectSize": {
                        "small": { "map50": 0.9, "map50_95": 0.65, "map75": 0.85 },
                        "medium": { "map50": 0.99, "map50_95": 0.85, "map75": 0.99 },
                        "large": null
                    }
                }
            }
        },
        "valid": { "...": "same shape" },
        "train": { "...": "same shape" }
    }
}

Response (semantic segmentation)

{
    "taskType": "semantic-segmentation",
    "splits": {
        "test": {
            "miou": 0.816,
            "precision": 0.938,
            "recall": 0.862,
            "f1": 0.898,
            "perClass": [
                {
                    "classID": 3,
                    "className": "multi",
                    "iou": 0.816,
                    "precision": 0.938,
                    "recall": 0.862,
                    "f1": 0.898,
                    "optimalThreshold": 0.0
                }
            ]
        },
        "valid": { "...": "same shape" },
        "train": { "...": "same shape" }
    }
}

Notes

• The taskType field discriminates the response shape. Always check it before parsing split contents.

• Detection: map50_95 is mAP averaged over IoU thresholds from 0.5 to 0.95 in steps of 0.05 (the COCO standard). Object-size buckets are null when the split contains no instances of that size. Per-class entries appear under perClass, keyed by class name.

• Semantic segmentation: All metrics are pixel-level macro means over foreground classes (background excluded). miou is the mean Intersection-over-Union. optimalThreshold is the per-class F1-optimal confidence threshold. A value of 0.0 is valid and means the model peaks at argmax.