YOLOv8-16bit/docs/modes/predict.md

YOLOv8 predict mode can generate predictions for various tasks, returning either a list of Results objects or a memory-efficient generator of Results objects when using the streaming mode. Enable streaming mode by passing stream=True in the predictor's call method.

!!! example "Predict"

=== "Return a list with `Stream=False`"
    ```python
    inputs = [img, img]  # list of numpy 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 segmentation masks outputs
        probs = result.probs  # Class probabilities for classification outputs
    ```

=== "Return a list with `Stream=True`"
    ```python
    inputs = [img, img]  # list of numpy arrays
    results = model(inputs, stream=True)  # generator of Results objects
    
    for result in results:
        boxes = result.boxes  # Boxes object for bbox outputs
        masks = result.masks  # Masks object for segmentation masks outputs
        probs = result.probs  # Class probabilities for classification outputs
    ```

!!! tip "Tip"

Streaming mode with `stream=True` should be used for long videos or large predict sources, otherwise results will accumuate in memory and will eventually cause out-of-memory errors. 

Sources

YOLOv8 can accept various input sources, as shown in the table below. This includes images, URLs, PIL images, OpenCV, numpy arrays, torch tensors, CSV files, videos, directories, globs, YouTube videos, and streams. The table indicates whether each source can be used in streaming mode with stream=True ✅ and an example argument for each source.

source	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 and Video Formats

YOLOv8 supports various image and video formats, as specified in yolo/data/utils.py. See the tables below for the valid suffixes and example predict commands.

Image Suffixes

Image Suffixes	Example Predict Command	Reference
.bmp	yolo predict source=image.bmp	Microsoft BMP File Format
.dng	yolo predict source=image.dng	Adobe DNG
.jpeg	yolo predict source=image.jpeg	JPEG
.jpg	yolo predict source=image.jpg	JPEG
.mpo	yolo predict source=image.mpo	Multi Picture Object
.png	yolo predict source=image.png	Portable Network Graphics
.tif	yolo predict source=image.tif	Tag Image File Format
.tiff	yolo predict source=image.tiff	Tag Image File Format
.webp	yolo predict source=image.webp	WebP
.pfm	yolo predict source=image.pfm	Portable FloatMap

Video Suffixes

Video Suffixes	Example Predict Command	Reference
.asf	yolo predict source=video.asf	Advanced Systems Format
.avi	yolo predict source=video.avi	Audio Video Interleave
.gif	yolo predict source=video.gif	Graphics Interchange Format
.m4v	yolo predict source=video.m4v	MPEG-4 Part 14
.mkv	yolo predict source=video.mkv	Matroska
.mov	yolo predict source=video.mov	QuickTime File Format
.mp4	yolo predict source=video.mp4	MPEG-4 Part 14 - Wikipedia
.mpeg	yolo predict source=video.mpeg	MPEG-1 Part 2
.mpg	yolo predict source=video.mpg	MPEG-1 Part 2
.ts	yolo predict source=video.ts	MPEG Transport Stream
.wmv	yolo predict source=video.wmv	Windows Media Video
.webm	yolo predict source=video.webm	WebM Project

Working with Results

The Results object contains the following components:

• Results.boxes: Boxes object with properties and methods for manipulating bounding boxes
• Results.masks: Masks object for indexing masks or getting segment coordinates
• Results.probs: torch.Tensor containing class probabilities or logits
• Results.orig_img: Original image loaded in memory
• Results.path: Path containing the path to the input image

Each result is composed of a torch.Tensor by default, which allows for easy manipulation:

!!! example "Results"

```python
results = results.cuda()
results = results.cpu()
results = results.to('cpu')
results = results.numpy()
```

Boxes

Boxes object can be used to index, manipulate, and convert bounding boxes to different formats. Box format conversion operations are cached, meaning they're only calculated once per object, and those values are reused for future calls.

• Indexing a Boxes object returns a Boxes object:

!!! example "Boxes"

```python
results = model(img)
boxes = results[0].boxes
box = boxes[0]  # returns one box
box.xyxy
```

• Properties and conversions

!!! example "Boxes Properties"

```python
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.

!!! example "Masks"

```python
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.

!!! example "Probs"

```python
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

!!! example "Plotting"

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

• 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