# Inspecting results

*Estimated read time: 3 minutes*

Every inference call returns an `InferenceResults` object. This page shows how to explore `result.results`, check metadata, and prepare the data for downstream logic such as filtering or aggregation. Each example below loads the model inline so you can copy and run sections independently.

## Check the overall structure

`InferenceResults` exposes high-level fields via attributes. Use `dir(result)` or directly access `result.results`, `result.info`, and `result.timing`.

### Example

{% code overflow="wrap" %}

```python
from degirum_tools import ModelSpec, remote_assets

model_spec = ModelSpec(
    model_name="yolov8n_coco--640x640_quant_hailort_multidevice_1",
    zoo_url="degirum/hailo",
    inference_host_address="@local",
    model_properties={"device_type": ["HAILORT/HAILO8L", "HAILORT/HAILO8"]},
)
model = model_spec.load_model()

result = model(remote_assets.three_persons)

print([attr for attr in dir(result) if not attr.startswith("_")])
print(type(result.results), len(result.results))
```

{% endcode %}

Example output:

{% code overflow="wrap" %}

```python
['image', 'image_model', 'image_overlay', 'info', 'results', 'timing']
<class 'list'> 3
```

{% endcode %}

Detection models return a list of dictionaries with keys such as `bbox`, `label`, and `score`. Classification models return ranked predictions; segmentation models can include masks. The exact schema depends on the postprocessor configured in your `ModelSpec`.

## Inspect detection outputs

Loop through `result.results` to extract bounding boxes and confidence scores. Convert floating-point values with `float()` if you plan to serialize or log them.

### Example

{% code overflow="wrap" %}

```python
from degirum_tools import ModelSpec, remote_assets

model_spec = ModelSpec(
    model_name="yolov8n_coco--640x640_quant_hailort_multidevice_1",
    zoo_url="degirum/hailo",
    inference_host_address="@local",
    model_properties={"device_type": ["HAILORT/HAILO8L", "HAILORT/HAILO8"]},
)
model = model_spec.load_model()

result = model(remote_assets.three_persons)

for detection in result.results:
    label = detection.get("label", "unknown")
    score = float(detection.get("score", 0))
    bbox = detection.get("bbox")  # [x_min, y_min, x_max, y_max]
    print(f"{label}: {score:.3f} box={bbox}")

people = [det for det in result.results if det.get("label") == "person"]
print(f"Detected {len(people)} person(s)")
```

{% endcode %}

Example output:

{% code overflow="wrap" %}

```python
person: 0.901 box=[0.13, 0.23, 0.35, 0.88]
person: 0.871 box=[0.52, 0.18, 0.75, 0.87]
person: 0.678 box=[0.74, 0.28, 0.96, 0.91]
Detected 3 person(s)
```

{% endcode %}

{% hint style="info" %}
Need to focus on a subset of classes? Filter the list before continuing with downstream logic.
{% endhint %}

## Read metadata and timing

When you pass metadata to `predict_batch` or `predict_stream`, it is surfaced via `result.info`. Timing data is recorded when you enable profiling in the model properties.

### Example

{% code overflow="wrap" %}

```python
from degirum_tools import ModelSpec, remote_assets

model_spec = ModelSpec(
    model_name="yolov8n_coco--640x640_quant_hailort_multidevice_1",
    zoo_url="degirum/hailo",
    inference_host_address="@local",
    model_properties={
        "device_type": ["HAILORT/HAILO8L", "HAILORT/HAILO8"],
        "postprocess": {"timing": {"enable": True}},
    },
)
model = model_spec.load_model()

batch_results = list(
    model.predict_batch(
        [remote_assets.three_persons],
        info_list=[{"camera_id": "demo-cam-01", "frame": 7}],
    )
)
result = batch_results[0]

print("Frame metadata:", result.info)
print("Timing (ms):", result.timing)
```

{% endcode %}

{% code overflow="wrap" %}

```python
Frame metadata: {'camera_id': 'demo-cam-01', 'frame': 7}
Timing (ms): {'preprocess': 2.87, 'inference': 34.68, 'postprocess': 4.09}
```

{% endcode %}