Fix keypoints.conf update Results docs (#2977)

docs/modes/predict.md

@@ -138,7 +138,8 @@ 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.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
 
@@ -178,8 +179,8 @@ operations are cached, meaning they're only calculated once per object, and thos
     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.conf  # confidence score, (N, )
+    boxes.cls  # cls, (N, )
     boxes.data  # raw bboxes tensor, (N, 6) or boxes.boxes
     ```
 
@@ -197,15 +198,35 @@ operations are cached, meaning they're only calculated once per object, and thos
     masks.data  # raw masks tensor, (N, H, W) or masks.masks 
     ```
 
+### Keypoints
+
+`Keypoints` object can be used index, manipulate and normalize coordinates. The keypoint conversion operation is cached.
+
+!!! 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) 
+    ```
+
 ### probs
 
-`probs` attribute of `Results` class is a `Tensor` containing class probabilities of a classification operation.
+`Probs` object can be used index, get top1&top5 indices and scores of classification.
 
 !!! example "Probs"
 
     ```python
     results = model(inputs)
-    results[0].probs  # cls prob, (num_class, )
+    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, ) 
     ```
 
 Class reference documentation for `Results` module and its components can be found [here](../reference/yolo/engine/results.md)
@@ -213,7 +234,7 @@ Class reference documentation for `Results` module and its components can be fou
 ## 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
+masks, classification probabilities, etc.) found in the results object
 
 !!! example "Plotting"
 

tests/test_python.py

@@ -220,9 +220,10 @@ def _test_results_api(res):
     res.plot(pil=True)
     res.plot(conf=True, boxes=False)
     res.plot()
+    print(res)
     print(res.path)
     for k in res.keys:
-        print(getattr(res, k).data)
+        print(getattr(res, k))
 
 
 def test_results():

ultralytics/yolo/engine/results.py

@@ -71,9 +71,9 @@ class Results(SimpleClass):
         orig_img (numpy.ndarray): The original image as a numpy array.
         path (str): The path to the image file.
         names (dict): A dictionary of class names.
-        boxes (List[List[float]], optional): A list of bounding box coordinates for each detection.
-        masks (numpy.ndarray, optional): A 3D numpy array of detection masks, where each mask is a binary image.
-        probs (numpy.ndarray, optional): A 2D numpy array of detection probabilities for each class.
+        boxes (torch.tensor, optional): A 2D tensor of bounding box coordinates for each detection.
+        masks (torch.tensor, optional): A 3D tensor of detection masks, where each mask is a binary image.
+        probs (torch.tensor, optional): A 1D tensor of probabilities of each class for classification task.
         keypoints (List[List[float]], optional): A list of detected keypoints for each object.
 
 
@@ -82,10 +82,10 @@ class Results(SimpleClass):
         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 (numpy.ndarray, optional): A 2D numpy array of detection probabilities for each class.
+        probs (Probs, optional): A Probs object containing probabilities of each class for classification task.
         names (dict): A dictionary of class names.
         path (str): The path to the image file.
-        keypoints (List[List[float]], optional): A list of detected keypoints for each object.
+        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.
         _keys (tuple): A tuple of attribute names for non-empty attributes.
     """
@@ -552,7 +552,7 @@ class Keypoints(BaseTensor):
     @property
     @lru_cache(maxsize=1)
     def conf(self):
-        return self.data[..., 3] if self.has_visible else None
+        return self.data[..., 2] if self.has_visible else None
 
 
 class Probs(BaseTensor):