YOLOv8-16bit/docs/modes/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"

=== "Return 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
```

=== "Return 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
```

Sources

YOLOv8 can run inference on a variety of sources. The table below lists the various sources that can be used as input for YOLOv8, along with the required format and notes. Sources include images, URLs, PIL images, OpenCV, numpy arrays, torch tensors, CSV files, videos, directories, globs, YouTube videos, and streams. The table also indicates whether each source can be used as a stream and the model argument required for that source.

source	stream	model(arg)	type	notes
image		'im.jpg'	str, Path
URL		'https://ultralytics.com/images/bus.jpg'	str
screenshot		'screen'	str
PIL		Image.open('im.jpg')	PIL.Image	HWC, RGB
OpenCV		cv2.imread('im.jpg')[:,:,::-1]	np.ndarray	HWC, BGR to RGB
numpy		np.zeros((640,1280,3))	np.ndarray	HWC
torch		torch.zeros(16,3,320,640)	torch.Tensor	BCHW, RGB
CSV		'sources.csv'	str, Path	RTSP, RTMP, HTTP
video	✓	'vid.mp4'	str, Path
directory	✓	'path/'	str, Path
glob	✓	'path/*.jpg'	str	Use * operator
YouTube	✓	'https://youtu.be/Zgi9g1ksQHc'	str
stream	✓	'rtsp://example.com/media.mp4'	str	RTSP, RTMP, HTTP

Image Formats

For images, YOLOv8 supports a variety of image formats defined in yolo/data/utils.py. The following suffixes are valid for images:

Image Suffixes	Example Predict Command	Reference
bmp	yolo predict source=image.bmp	Microsoft
dng	yolo predict source=image.dng	Adobe
jpeg	yolo predict source=image.jpeg	Joint Photographic Experts Group
jpg	yolo predict source=image.jpg	Joint Photographic Experts Group
mpo	yolo predict source=image.mpo	CIPA
png	yolo predict source=image.png	Portable Network Graphics
tif	yolo predict source=image.tif	Adobe
tiff	yolo predict source=image.tiff	Adobe
webp	yolo predict source=image.webp	Google Developers
pfm	yolo predict source=image.pfm	HDR Labs

Video Formats

For videos, YOLOv8 also supports a variety of video formats defined in yolo/data/utils.py. The following suffixes are valid for videos:

Video Suffixes	Example Predict Command	Reference
asf	yolo predict source=video.asf	Microsoft
avi	yolo predict source=video.avi	Microsoft
gif	yolo predict source=video.gif	CompuServe
m4v	yolo predict source=video.m4v	Apple
mkv	yolo predict source=video.mkv	Matroska
mov	yolo predict source=video.mov	Apple
mp4	yolo predict source=video.mp4	ISO 68939
mpeg	yolo predict source=video.mpeg	ISO 56021
mpg	yolo predict source=video.mpg	ISO 56021
ts	yolo predict source=video.ts	MPEG Transport Stream
wmv	yolo predict source=video.wmv	Microsoft
webm	yolo predict source=video.webm	Google Developers

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_img: Original image loaded in memory.
• Results.path: Path containing the path to input image

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

Plotting results

You can use plot() function of Result object to plot results on in image object. It plots all components(boxes, masks, classification logits, etc) found in the results object

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

!!! example "plot() 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