YOLOv8-16bit/docs/predict.md

Inference or prediction of a task returns a list of Results objects. Alternatively, in the streaming mode, it returns a generator of Results objects which is memory efficient. Streaming mode can be enabled by passing stream=True in predictor's call method.

!!! example "Predict"

=== "Getting a List"

```python
inputs = [img, img]  # list of np arrays
results = model(inputs)  # List of Results objects

for result in results:
    boxes = result.boxes  # Boxes object for bbox outputs
    masks = result.masks  # Masks object for segmenation masks outputs
    probs = result.probs  # Class probabilities for classification outputs
```

=== "Getting a Generator"

```python
inputs = [img, img]  # list of numpy arrays
results = model(inputs, stream=True)  # generator of Results objects

for r in results:
    boxes = r.boxes  # Boxes object for bbox outputs
    masks = r.masks  # Masks object for segmenation masks outputs
    probs = r.probs  # Class probabilities for classification outputs
```

Working with Results

Results object consists of these component objects:

• Results.boxes : Boxes object with properties and methods for manipulating bboxes
• Results.masks : Masks object used to index masks or to get segment coordinates.
• Results.probs : torch.Tensor containing the class probabilities/logits.
• Results.orig_shape : tuple containing the original image size as (height, width).

Each result is composed of torch.Tensor by default, in which you can easily use following functionality:

results = results.cuda()
results = results.cpu()
results = results.to("cpu")
results = results.numpy()

Boxes

Boxes object can be used index, manipulate and convert bboxes to different formats. The box format conversion operations are cached, which means they're only calculated once per object and those values are reused for future calls.

• Indexing a Boxes objects returns a Boxes object

results = model(inputs)
boxes = results[0].boxes
box = boxes[0]  # returns one box
box.xyxy 

• Properties and conversions

boxes.xyxy  # box with xyxy format, (N, 4)
boxes.xywh  # box with xywh format, (N, 4)
boxes.xyxyn  # box with xyxy format but normalized, (N, 4)
boxes.xywhn  # box with xywh format but normalized, (N, 4)
boxes.conf  # confidence score, (N, 1)
boxes.cls  # cls, (N, 1)
boxes.data  # raw bboxes tensor, (N, 6) or boxes.boxes .

Masks

Masks object can be used index, manipulate and convert masks to segments. The segment conversion operation is cached.

results = model(inputs)
masks = results[0].masks  # Masks object
masks.segments  # bounding coordinates of masks, List[segment] * N
masks.data  # raw masks tensor, (N, H, W) or masks.masks 

probs

probs attribute of Results class is a Tensor containing class probabilities of a classification operation.

results = model(inputs)
results[0].probs  # cls prob, (num_class, )

Class reference documentation for Results module and its components can be found here

Visualizing results

You can use visualize() function of Result object to get a visualization. It plots all components(boxes, masks, classification logits, etc) found in the results object

    res = model(img)
    res_plotted = res[0].visualize()
    cv2.imshow("result", res_plotted)

!!! example "visualize() arguments"

`show_conf (bool)`: Show confidence

`line_width (Float)`: The line width of boxes. Automatically scaled to img size if not provided

`font_size (Float)`: The font size of . Automatically scaled to img size if not provided