ultralytics 8.0.42 DDP fix and Docs updates (#1065)

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docs/predict.md

@@ -32,10 +32,11 @@ predictor's call method.
 
 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).
+- `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:
 
@@ -94,18 +95,18 @@ results[0].probs  # cls prob, (num_class, )
 
 Class reference documentation for `Results` module and its components can be found [here](reference/results.md)
 
-## Visualizing results
+## Plotting results
 
-You can use `visualize()` function of `Result` object to get a visualization. It plots all components(boxes, masks,
+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
 
 ```python
-    res = model(img)
-    res_plotted = res[0].visualize()
-    cv2.imshow("result", res_plotted)
+res = model(img)
+res_plotted = res[0].plot()
+cv2.imshow("result", res_plotted)
 ```
 
-!!! example "`visualize()` arguments"
+!!! example "`plot()` arguments"
 
     `show_conf (bool)`: Show confidence
 

docs/tasks/tracking.md

@@ -0,0 +1,86 @@
+Object tracking is a task that involves identifying the location and class of objects, then assigning a unique ID to
+that detection in video streams.
+
+The output of tracker is the same as detection with an added object ID.
+
+## Available Trackers
+
+The following tracking algorithms have been implemented and can be enabled by passing `tracker=tracker_type.yaml`
+
+* [BoT-SORT](https://github.com/NirAharon/BoT-SORT) - `botsort.yaml`
+* [ByteTrack](https://github.com/ifzhang/ByteTrack) - `bytetrack.yaml`
+
+The default tracker is BoT-SORT.
+
+## Tracking
+
+Use a trained YOLOv8n/YOLOv8n-seg model to run tracker on video streams.
+
+!!! example ""
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+        
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load an official detection model
+        model = YOLO("yolov8n-seg.pt")  # load an official segmentation model
+        model = YOLO("path/to/best.pt")  # load a custom model
+        
+        # Track with the model
+        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True) 
+        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True, tracker="bytetrack.yaml") 
+        ```
+    === "CLI"
+    
+        ```bash
+        yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc"  # official detection model
+        yolo track model=yolov8n-seg.pt source=...   # official segmentation model
+        yolo track model=path/to/best.pt source=...  # custom model
+        yolo track model=path/to/best.pt  tracker="bytetrack.yaml" # bytetrack tracker
+
+        ```
+
+As in the above usage, we support both the detection and segmentation models for tracking and the only thing you need to do is loading the corresponding(detection or segmentation) model.
+
+## Configuration
+### Tracking
+Tracking shares the configuration with predict, i.e `conf`, `iou`, `show`. More configurations please refer to [predict page](https://docs.ultralytics.com/cfg/#prediction).
+!!! example ""
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+        
+        model = YOLO("yolov8n.pt")
+        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True) 
+        ```
+    === "CLI"
+    
+        ```bash
+        yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" conf=0.3, iou=0.5 show
+
+        ```
+
+### Tracker
+We also support using a modified tracker config file, just copy a config file i.e `custom_tracker.yaml` from [ultralytics/tracker/cfg](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/tracker/cfg) and modify any configurations(expect the `tracker_type`) you need to.
+!!! example ""
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+        
+        model = YOLO("yolov8n.pt")
+        results = model.track(source="https://youtu.be/Zgi9g1ksQHc", tracker='custom_tracker.yaml') 
+        ```
+    === "CLI"
+    
+        ```bash
+        yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" tracker='custom_tracker.yaml'
+
+        ```
+Please refer to [ultralytics/tracker/cfg](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/tracker/cfg) page. 
+