docs for faster+mask rcnn coords is clearer (#1868)

Co-authored-by: rvirgolireply <51229032+rvirgolireply@users.noreply.github.com>

torchvision/models/detection/faster_rcnn.py

@@ -32,8 +32,8 @@ class FasterRCNN(GeneralizedRCNN):
 
     During training, the model expects both the input tensors, as well as a targets (list of dictionary),
     containing:
-        - boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with values
-          between 0 and H and 0 and W
+        - boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with values of x
+          between 0 and W and values of y between 0 and H
         - labels (Int64Tensor[N]): the class label for each ground-truth box
 
     The model returns a Dict[Tensor] during training, containing the classification and regression
@@ -42,8 +42,8 @@ class FasterRCNN(GeneralizedRCNN):
     During inference, the model requires only the input tensors, and returns the post-processed
     predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as
     follows:
-        - boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between
-          0 and H and 0 and W
+        - boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values of x
+          between 0 and W and values of y between 0 and H
         - labels (Int64Tensor[N]): the predicted labels for each image
         - scores (Tensor[N]): the scores or each prediction
 
@@ -300,8 +300,8 @@ def fasterrcnn_resnet50_fpn(pretrained=False, progress=True,
 
     During training, the model expects both the input tensors, as well as a targets (list of dictionary),
     containing:
-        - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with values
-          between ``0`` and ``H`` and ``0`` and ``W``
+        - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with values of ``x``
+          between ``0`` and ``W`` and values of ``y`` between ``0`` and ``H``
         - labels (``Int64Tensor[N]``): the class label for each ground-truth box
 
     The model returns a ``Dict[Tensor]`` during training, containing the classification and regression
@@ -310,8 +310,8 @@ def fasterrcnn_resnet50_fpn(pretrained=False, progress=True,
     During inference, the model requires only the input tensors, and returns the post-processed
     predictions as a ``List[Dict[Tensor]]``, one for each input image. The fields of the ``Dict`` are as
     follows:
-        - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with values between
-          ``0`` and ``H`` and ``0`` and ``W``
+        - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with values of ``x``
+          between ``0`` and ``W`` and values of ``y`` between ``0`` and ``H``
         - labels (``Int64Tensor[N]``): the predicted labels for each image
         - scores (``Tensor[N]``): the scores or each prediction
 

torchvision/models/detection/mask_rcnn.py

@@ -28,8 +28,8 @@ class MaskRCNN(FasterRCNN):
 
     During training, the model expects both the input tensors, as well as a targets (list of dictionary),
     containing:
-        - boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with values
-          between 0 and H and 0 and W
+        - boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with values of x
+          between 0 and W and values of y between 0 and H
         - labels (Int64Tensor[N]): the class label for each ground-truth box
         - masks (UInt8Tensor[N, H, W]): the segmentation binary masks for each instance
 
@@ -39,8 +39,8 @@ class MaskRCNN(FasterRCNN):
     During inference, the model requires only the input tensors, and returns the post-processed
     predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as
     follows:
-        - boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between
-          0 and H and 0 and W
+        - boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values of x
+          between 0 and W and values of y between 0 and H
         - labels (Int64Tensor[N]): the predicted labels for each image
         - scores (Tensor[N]): the scores or each prediction
         - masks (UInt8Tensor[N, 1, H, W]): the predicted masks for each instance, in 0-1 range. In order to
@@ -276,8 +276,8 @@ def maskrcnn_resnet50_fpn(pretrained=False, progress=True,
 
     During training, the model expects both the input tensors, as well as a targets (list of dictionary),
     containing:
-        - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with values
-          between ``0`` and ``H`` and ``0`` and ``W``
+        - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format,  with values of ``x``
+          between ``0`` and ``W`` and values of ``y`` between ``0`` and ``H``
         - labels (``Int64Tensor[N]``): the class label for each ground-truth box
         - masks (``UInt8Tensor[N, H, W]``): the segmentation binary masks for each instance
 
@@ -287,8 +287,8 @@ def maskrcnn_resnet50_fpn(pretrained=False, progress=True,
     During inference, the model requires only the input tensors, and returns the post-processed
     predictions as a ``List[Dict[Tensor]]``, one for each input image. The fields of the ``Dict`` are as
     follows:
-        - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with values between
-          ``0`` and ``H`` and ``0`` and ``W``
+        - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format,  with values of ``x``
+          between ``0`` and ``W`` and values of ``y`` between ``0`` and ``H``
         - labels (``Int64Tensor[N]``): the predicted labels for each image
         - scores (``Tensor[N]``): the scores or each prediction
         - masks (``UInt8Tensor[N, 1, H, W]``): the predicted masks for each instance, in ``0-1`` range. In order to