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Update prediction Results docs (#4139)

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@ -6,9 +6,7 @@ keywords: Ultralytics, YOLOv8, predict mode, inference sources, prediction tasks
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
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.
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"
@ -27,7 +25,7 @@ passing `stream=True` in the predictor's call method.
boxes = result.boxes # Boxes object for bbox outputs
masks = result.masks # Masks object for segmentation masks outputs
keypoints = result.keypoints # Keypoints object for pose outputs
probs = result.probs # Class probabilities for classification outputs
probs = result.probs # Probs object for classification outputs
```
=== "Return a generator with `stream=True`"
@ -45,7 +43,7 @@ passing `stream=True` in the predictor's call method.
boxes = result.boxes # Boxes object for bbox outputs
masks = result.masks # Masks object for segmentation masks outputs
keypoints = result.keypoints # Keypoints object for pose outputs
probs = result.probs # Class probabilities for classification outputs
probs = result.probs # Probs object for classification outputs
```
## Inference Sources
@ -281,45 +279,52 @@ Below are code examples for using each source type:
## Inference Arguments
`model.predict` accepts multiple arguments that control the prediction operation. These arguments can be passed directly to `model.predict`:
`model.predict()` accepts multiple arguments that can be passed at inference time to override defaults:
!!! example
```python
model.predict(source, save=True, imgsz=320, conf=0.5)
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Run inference on 'bus.jpg' with arguments
model.predict('bus.jpg', save=True, imgsz=320, conf=0.5)
```
All supported arguments:
| Key | Value | Description |
|----------------|------------------------|--------------------------------------------------------------------------------|
| `source` | `'ultralytics/assets'` | source directory for images or videos |
| `conf` | `0.25` | object confidence threshold for detection |
| `iou` | `0.7` | intersection over union (IoU) threshold for NMS |
| `imgsz` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) |
| `half` | `False` | use half precision (FP16) |
| `device` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu |
| `show` | `False` | show results if possible |
| `save` | `False` | save images with results |
| `save_txt` | `False` | save results as .txt file |
| `save_conf` | `False` | save results with confidence scores |
| `save_crop` | `False` | save cropped images with results |
| `hide_labels` | `False` | hide labels |
| `hide_conf` | `False` | hide confidence scores |
| `max_det` | `300` | maximum number of detections per image |
| `vid_stride` | `False` | video frame-rate stride |
| `line_width` | `None` | The line width of the bounding boxes. If None, it is scaled to the image size. |
| `visualize` | `False` | visualize model features |
| `augment` | `False` | apply image augmentation to prediction sources |
| `agnostic_nms` | `False` | class-agnostic NMS |
| `retina_masks` | `False` | use high-resolution segmentation masks |
| `classes` | `None` | filter results by class, i.e. class=0, or class=[0,2,3] |
| `boxes` | `True` | Show boxes in segmentation predictions |
| Name | Type | Default | Description |
|----------------|----------------|------------------------|--------------------------------------------------------------------------------|
| `source` | `str` | `'ultralytics/assets'` | source directory for images or videos |
| `conf` | `float` | `0.25` | object confidence threshold for detection |
| `iou` | `float` | `0.7` | intersection over union (IoU) threshold for NMS |
| `imgsz` | `int or tuple` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) |
| `half` | `bool` | `False` | use half precision (FP16) |
| `device` | `None or str` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu |
| `show` | `bool` | `False` | show results if possible |
| `save` | `bool` | `False` | save images with results |
| `save_txt` | `bool` | `False` | save results as .txt file |
| `save_conf` | `bool` | `False` | save results with confidence scores |
| `save_crop` | `bool` | `False` | save cropped images with results |
| `hide_labels` | `bool` | `False` | hide labels |
| `hide_conf` | `bool` | `False` | hide confidence scores |
| `max_det` | `int` | `300` | maximum number of detections per image |
| `vid_stride` | `bool` | `False` | video frame-rate stride |
| `line_width` | `None or int` | `None` | The line width of the bounding boxes. If None, it is scaled to the image size. |
| `visualize` | `bool` | `False` | visualize model features |
| `augment` | `bool` | `False` | apply image augmentation to prediction sources |
| `agnostic_nms` | `bool` | `False` | class-agnostic NMS |
| `retina_masks` | `bool` | `False` | use high-resolution segmentation masks |
| `classes` | `None or list` | `None` | filter results by class, i.e. class=0, or class=[0,2,3] |
| `boxes` | `bool` | `True` | Show boxes in segmentation predictions |
## Image and Video Formats
YOLOv8 supports various image and video formats, as specified in [data/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/utils.py). See the tables below for the valid suffixes and example predict commands.
### Image Suffixes
### Images
The below table contains valid Ultralytics image formats.
@ -336,7 +341,7 @@ The below table contains valid Ultralytics image formats.
| .webp | `yolo predict source=image.webp` | [WebP](https://en.wikipedia.org/wiki/WebP) |
| .pfm | `yolo predict source=image.pfm` | [Portable FloatMap](https://en.wikipedia.org/wiki/Netpbm#File_formats) |
### Video Suffixes
### Videos
The below table contains valid Ultralytics video formats.
@ -357,129 +362,235 @@ The below table contains valid Ultralytics video formats.
## 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.keypoints`: `Keypoints` object for with properties and methods for manipulating predicted keypoints.
- `Results.probs`: `Probs` object for containing class probabilities.
- `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:
All Ultralytics `predict()` calls will return a list of `Results` objects:
!!! example "Results"
```python
results = results.cuda()
results = results.cpu()
results = results.to('cpu')
results = results.numpy()
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Run inference on an image
results = model('bus.jpg') # list of 1 Results object
results = model(['bus.jpg', 'zidane.jpg']) # list of 2 Results objects
```
`Results` objects have the following attributes:
| Attribute | Type | Description |
|--------------|-----------------------|------------------------------------------------------------------------------------------|
| `orig_img` | `numpy.ndarray` | The original image as a numpy array. |
| `orig_shape` | `tuple` | The original image shape in (height, width) format. |
| `boxes` | `Boxes, optional` | A Boxes object containing the detection bounding boxes. |
| `masks` | `Masks, optional` | A Masks object containing the detection masks. |
| `probs` | `Probs, optional` | A Probs object containing probabilities of each class for classification task. |
| `keypoints` | `Keypoints, optional` | A Keypoints object containing detected keypoints for each object. |
| `speed` | `dict` | A dictionary of preprocess, inference, and postprocess speeds in milliseconds per image. |
| `names` | `dict` | A dictionary of class names. |
| `path` | `str` | The path to the image file. |
`Results` objects have the following methods:
| Method | Return Type | Description |
|-----------------|-----------------|-------------------------------------------------------------------------------------|
| `__getitem__()` | `Results` | Return a Results object for the specified index. |
| `__len__()` | `int` | Return the number of detections in the Results object. |
| `update()` | `None` | Update the boxes, masks, and probs attributes of the Results object. |
| `cpu()` | `Results` | Return a copy of the Results object with all tensors on CPU memory. |
| `numpy()` | `Results` | Return a copy of the Results object with all tensors as numpy arrays. |
| `cuda()` | `Results` | Return a copy of the Results object with all tensors on GPU memory. |
| `to()` | `Results` | Return a copy of the Results object with tensors on the specified device and dtype. |
| `new()` | `Results` | Return a new Results object with the same image, path, and names. |
| `keys()` | `List[str]` | Return a list of non-empty attribute names. |
| `plot()` | `numpy.ndarray` | Plots the detection results. Returns a numpy array of the annotated image. |
| `verbose()` | `str` | Return log string for each task. |
| `save_txt()` | `None` | Save predictions into a txt file. |
| `save_crop()` | `None` | Save cropped predictions to `save_dir/cls/file_name.jpg`. |
| `tojson()` | `None` | Convert the object to JSON format. |
For more details see the `Results` class [documentation](../reference/engine/results.md#-results).
### 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:
`Boxes` object can be used to index, manipulate, and convert bounding boxes to different formats.
!!! example "Boxes"
```python
results = model(img)
boxes = results[0].boxes
box = boxes[0] # returns one box
box.xyxy
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Run inference on an image
results = model('bus.jpg') # results list
# View results
for r in results:
print(r.boxes) # print the Boxes object containing the detection bounding boxes
```
- Properties and conversions
Here is a table for the `Boxes` class methods and properties, including their name, type, and description:
!!! example "Boxes Properties"
| Name | Type | Description |
|-----------|---------------------------|--------------------------------------------------------------------|
| `cpu()` | Method | Move the object to CPU memory. |
| `numpy()` | Method | Convert the object to a numpy array. |
| `cuda()` | Method | Move the object to CUDA memory. |
| `to()` | Method | Move the object to the specified device. |
| `xyxy` | Property (`torch.Tensor`) | Return the boxes in xyxy format. |
| `conf` | Property (`torch.Tensor`) | Return the confidence values of the boxes. |
| `cls` | Property (`torch.Tensor`) | Return the class values of the boxes. |
| `id` | Property (`torch.Tensor`) | Return the track IDs of the boxes (if available). |
| `xywh` | Property (`torch.Tensor`) | Return the boxes in xywh format. |
| `xyxyn` | Property (`torch.Tensor`) | Return the boxes in xyxy format normalized by original image size. |
| `xywhn` | Property (`torch.Tensor`) | Return the boxes in xywh format normalized by original image size. |
```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, )
boxes.cls # cls, (N, )
boxes.data # raw bboxes tensor, (N, 6) or boxes.boxes
```
For more details see the `Boxes` class [documentation](../reference/engine/results.md#boxes).
### Masks
`Masks` object can be used index, manipulate and convert masks to segments. The segment conversion operation is cached.
`Masks` object can be used index, manipulate and convert masks to segments.
!!! example "Masks"
```python
results = model(inputs)
masks = results[0].masks # Masks object
masks.xy # x, y segments (pixels), List[segment] * N
masks.xyn # x, y segments (normalized), List[segment] * N
masks.data # raw masks tensor, (N, H, W) or masks.masks
from ultralytics import YOLO
# Load a pretrained YOLOv8n-seg Segment model
model = YOLO('yolov8n-seg.pt')
# Run inference on an image
results = model('bus.jpg') # results list
# View results
for r in results:
print(r.masks) # print the Masks object containing the detected instance masks
```
Here is a table for the `Masks` class methods and properties, including their name, type, and description:
| Name | Type | Description |
|------------|---------------------------|-----------------------------------------------------------------|
| `cpu()` | Method | Returns the masks tensor on CPU memory. |
| `numpy()` | Method | Returns the masks tensor as a numpy array. |
| `cuda()` | Method | Returns the masks tensor on GPU memory. |
| `to()` | Method | Returns the masks tensor with the specified device and dtype. |
| `xyn` | Property (`torch.Tensor`) | A list of normalized segments represented as tensors. |
| `xy` | Property (`torch.Tensor`) | A list of segments in pixel coordinates represented as tensors. |
For more details see the `Masks` class [documentation](../reference/engine/results.md#masks).
### Keypoints
`Keypoints` object can be used index, manipulate and normalize coordinates. The keypoint conversion operation is cached.
`Keypoints` object can be used index, manipulate and normalize coordinates.
!!! example "Keypoints"
```python
results = model(inputs)
keypoints = results[0].keypoints # Masks object
keypoints.xy # x, y keypoints (pixels), (num_dets, num_kpts, 2/3), the last dimension can be 2 or 3, depends the model.
keypoints.xyn # x, y keypoints (normalized), (num_dets, num_kpts, 2/3)
keypoints.conf # confidence score(num_dets, num_kpts) of each keypoint if the last dimension is 3.
keypoints.data # raw keypoints tensor, (num_dets, num_kpts, 2/3)
from ultralytics import YOLO
# Load a pretrained YOLOv8n-pose Pose model
model = YOLO('yolov8n-pose.pt')
# Run inference on an image
results = model('bus.jpg') # results list
# View results
for r in results:
print(r.keypoints) # print the Keypoints object containing the detected keypoints
```
### probs
Here is a table for the `Keypoints` class methods and properties, including their name, type, and description:
`Probs` object can be used index, get top1&top5 indices and scores of classification.
| Name | Type | Description |
|-----------|---------------------------|-------------------------------------------------------------------|
| `cpu()` | Method | Returns the keypoints tensor on CPU memory. |
| `numpy()` | Method | Returns the keypoints tensor as a numpy array. |
| `cuda()` | Method | Returns the keypoints tensor on GPU memory. |
| `to()` | Method | Returns the keypoints tensor with the specified device and dtype. |
| `xyn` | Property (`torch.Tensor`) | A list of normalized keypoints represented as tensors. |
| `xy` | Property (`torch.Tensor`) | A list of keypoints in pixel coordinates represented as tensors. |
| `conf` | Property (`torch.Tensor`) | Returns confidence values of keypoints if available, else None. |
For more details see the `Keypoints` class [documentation](../reference/engine/results.md#keypoints).
### Probs
`Probs` object can be used index, get `top1` and `top5` indices and scores of classification.
!!! example "Probs"
```python
results = model(inputs)
probs = results[0].probs # cls prob, (num_class, )
probs.top5 # The top5 indices of classification, List[Int] * 5.
probs.top1 # The top1 indices of classification, a value with Int type.
probs.top5conf # The top5 scores of classification, a tensor with shape (5, ).
probs.top1conf # The top1 scores of classification. a value with torch.tensor type.
keypoints.data # raw probs tensor, (num_class, )
from ultralytics import YOLO
# Load a pretrained YOLOv8n-cls Classify model
model = YOLO('yolov8n-cls.pt')
# Run inference on an image
results = model('bus.jpg') # results list
# View results
for r in results:
print(r.probs) # print the Probs object containing the detected class probabilities
```
Class reference documentation for `Results` module and its components can be found [here](../reference/engine/results.md)
Here's a table summarizing the methods and properties for the `Probs` class:
## Plotting results
| Name | Type | Description |
|------------|-------------------------|-------------------------------------------------------------------------|
| `cpu()` | Method | Returns a copy of the probs tensor on CPU memory. |
| `numpy()` | Method | Returns a copy of the probs tensor as a numpy array. |
| `cuda()` | Method | Returns a copy of the probs tensor on GPU memory. |
| `to()` | Method | Returns a copy of the probs tensor with the specified device and dtype. |
| `top1` | Property `int` | Index of the top 1 class. |
| `top5` | Property `list[int]` | Indices of the top 5 classes. |
| `top1conf` | Property `torch.Tensor` | Confidence of the top 1 class. |
| `top5conf` | Property `torch.Tensor` | Confidences of the top 5 classes. |
You can use `plot()` function of `Result` object to plot results on in image object. It plots all components(boxes,
masks, classification probabilities, etc.) found in the results object
For more details see the `Probs` class [documentation](../reference/engine/results.md#probs).
## Plotting Results
You can the `plot()` method of a `Result` objects to plot predictions. It plots all prediction types (boxes, masks, keypoints, probabilities, etc.) contained in the `Results` object.
!!! example "Plotting"
```python
res = model(img)
res_plotted = res[0].plot()
cv2.imshow("result", res_plotted)
```
from PIL import Image
from ultralytics import YOLO
| Argument | Description |
|-------------------------------|----------------------------------------------------------------------------------------|
| `conf (bool)` | Whether to plot the detection confidence score. |
| `line_width (int, optional)` | The line width of the bounding boxes. If None, it is scaled to the image size. |
| `font_size (float, optional)` | The font size of the text. If None, it is scaled to the image size. |
| `font (str)` | The font to use for the text. |
| `pil (bool)` | Whether to use PIL for image plotting. |
| `example (str)` | An example string to display. Useful for indicating the expected format of the output. |
| `img (numpy.ndarray)` | Plot to another image. if not, plot to original image. |
| `labels (bool)` | Whether to plot the label of bounding boxes. |
| `boxes (bool)` | Whether to plot the bounding boxes. |
| `masks (bool)` | Whether to plot the masks. |
| `probs (bool)` | Whether to plot classification probability. |
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Run inference on 'bus.jpg'
results = model('bus.jpg') # results list
# Show the results
for r in results:
im = r.plot() # plot a BGR numpy array of predictions
Image.fromarray(im[..., ::-1]).show() # show RGB image
```
The `plot()` method has the following arguments available:
| Argument | Type | Description | Default |
|--------------|-----------------|--------------------------------------------------------------------------------|---------------|
| `conf` | `bool` | Whether to plot the detection confidence score. | `True` |
| `line_width` | `float` | The line width of the bounding boxes. If None, it is scaled to the image size. | `None` |
| `font_size` | `float` | The font size of the text. If None, it is scaled to the image size. | `None` |
| `font` | `str` | The font to use for the text. | `'Arial.ttf'` |
| `pil` | `bool` | Whether to return the image as a PIL Image. | `False` |
| `img` | `numpy.ndarray` | Plot to another image. if not, plot to original image. | `None` |
| `im_gpu` | `torch.Tensor` | Normalized image in gpu with shape (1, 3, 640, 640), for faster mask plotting. | `None` |
| `kpt_radius` | `int` | Radius of the drawn keypoints. Default is 5. | `5` |
| `kpt_line` | `bool` | Whether to draw lines connecting keypoints. | `True` |
| `labels` | `bool` | Whether to plot the label of bounding boxes. | `True` |
| `boxes` | `bool` | Whether to plot the bounding boxes. | `True` |
| `masks` | `bool` | Whether to plot the masks. | `True` |
| `probs` | `bool` | Whether to plot classification probability | `True` |
## Streaming Source `for`-loop