> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/developer/rest-api/model-evaluations/performance-by-class.md).

# Performance by Class

Returns per-class headline metrics for one split. The response shape depends on the evaluation's task type:

* **Object detection / instance segmentation** - per-class `map50`, `map50_95`, `map75`, precision, recall, F1, and optimal threshold.
* **Semantic segmentation** - per-class `iou`, precision, recall, F1, and optimal threshold (pixel-level).

The `taskType` field in the response indicates which shape to expect.

This is the data the **performance by class** panel in the app reads.

```url
https://api.roboflow.com/:workspace/model-evals/:evalId/performance-by-class
```

```bash
curl "https://api.roboflow.com/my-workspace/model-evals/$EVAL_ID/performance-by-class?api_key=$ROBOFLOW_API_KEY&split=test"
```

## Query parameters

| Parameter | Type | Description                                                                                                                        |
| --------- | ---- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `split`   | enum | One of `train`, `valid`, `test`. Default `test`. `all` is **not** valid here - per-class metrics are not aggregable across splits. |

## Response (object detection / instance segmentation)

```json
{
    "taskType": "object-detection-like",
    "split": "test",
    "classes": [
        {
            "className": "Car-rims",
            "map50": 0.9239650566041828,
            "map50_95": 0.7555258345429926,
            "map75": 0.9239650566041828,
            "precision": 0.85,
            "recall": 0.85,
            "f1": 0.85,
            "optimalThreshold": 0.37
        },
        {
            "className": "music-note",
            "map50": null,
            "map50_95": null,
            "map75": null,
            "precision": 0,
            "recall": 0,
            "f1": 0,
            "optimalThreshold": 0.5
        }
    ]
}
```

## Response (semantic segmentation)

```json
{
    "taskType": "semantic-segmentation",
    "split": "test",
    "classes": [
        {
            "classID": 3,
            "className": "multi",
            "iou": 0.816,
            "precision": 0.938,
            "recall": 0.862,
            "f1": 0.898,
            "optimalThreshold": 0.0
        }
    ]
}
```

## Notes

* `taskType` discriminates the per-class field set. Detection classes include `map50`/`map50_95`/`map75`; semantic segmentation classes include `iou` and `classID` instead.
* `optimalThreshold` is the per-class F1-optimal confidence threshold from the confidence sweep.
* `precision`, `recall`, and `f1` are reported at that per-class optimal threshold.
* For detection, mAP fields are `null` when the split has no instances of that class.
* For semantic segmentation, all metrics are pixel-level. An `optimalThreshold` of `0.0` is valid.


---

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