Small grammar and spelling fixes (#357)

README.md

@@ -38,7 +38,7 @@ ______________________________________________________________________
 
 > object detection should not be more difficult than classification, and should not require complex libraries for training and inference.
 
-`yolort` is very simple to implement and experiment with. You like the implementation of torchvision's faster-rcnn, retinanet or detr? You like yolov5? You love `yolort`!
+`yolort` is very simple to implement and experiment with. Do you like the implementation of torchvision's faster-rcnn, retinanet or detr? Do you like yolov5? You'll love `yolort`!
 
 <a href="notebooks/assets/zidane.jpg"><img src="notebooks/assets/zidane.jpg" alt="YOLO inference demo" width="500"/></a>
 

notebooks/inference-pytorch-export-libtorch.ipynb

@@ -235,7 +235,7 @@
    "source": [
     "## Detection output visualisation\n",
     "\n",
-    "First let get the labels of COCO datasets."
+    "First let's get the labels of COCO datasets."
    ]
   },
   {

yolort/models/transform.py

@@ -180,7 +180,7 @@ def forward(
         if targets is not None:
             # make a copy of targets to avoid modifying it in-place
             # once torchscript supports dict comprehension
-            # this can be simplified as as follows
+            # this can be simplified as follows
             # targets = [{k: v for k,v in t.items()} for t in targets]
             targets_copy: List[Dict[str, Tensor]] = []
             for t in targets:
@@ -305,7 +305,7 @@ def max_by_axis(self, the_list: List[List[int]]) -> List[int]:
 
     def batch_images(self, images: List[Tensor]) -> Tensor:
         """
-        Nest a list of tensors. It plays the same role of the lettebox function.
+        Nest a list of tensors. It plays the same role of the letterbox function.
         """
 
         if torchvision._is_tracing():