# 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 %}


---

# Agent Instructions: Querying This Documentation

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Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.degirum.com/hailo/basics/inference-results/inspecting-results.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.