Update README.md (#932)

Co-authored-by: dbogunowicz <97082108+dbogunowicz@users.noreply.github.com>

examples/vit_pose/README.md

@@ -14,39 +14,41 @@ See the License for the specific language governing permissions and
 limitations under the License.
 -->
 
-# Exploration for including the ViT Pose model into the DeepSparse pipeline
+# Exploration for Including the ViT Pose Model into the DeepSparse Pipeline
 Source: https://github.com/ViTAE-Transformer/ViTPose
 
 ## Installation
 Follow the instructions in the `ViTPose` README file. Note:
 
-- installing one of the dependencies, `mmcv` takes a lot of time and may look often like it is stuck. Be patient (or run with `-v` if helps), it will eventually terminate successfully.
-- after the setup completes, it is also advisable to downgrade the default torch version from `1.3` to `1.2` to avoid CUDA errors (as I am writing this, we are internally supporting `torch==1.2.1`)
+- Installing one of the dependencies, `mmcv` takes a lot of time and often may appear to pause. Be patient (or run with `-v`). Eventually, it will terminate successfully.
+- After the setup completes, it is advisable to downgrade the default torch version from `1.3` to `1.2` to avoid CUDA errors (currently, we are internally supporting `torch==1.2.1`).
 
 ## Export
 
-Before running the onnx export script, (manually) install `timm`, `onnx` and `onnxruntime`. Then, launch the [export script](https://github.com/ViTAE-Transformer/ViTPose/blob/main/tools/deployment/pytorch2onnx.py):
+Before running the ONNX export script, (manually) install `timm`, `onnx`, and `onnxruntime`. Then, launch the [export script](https://github.com/ViTAE-Transformer/ViTPose/blob/main/tools/deployment/pytorch2onnx.py):
 
 ```bash
 python tools/deployment/pytorch2onnx.py /home/ubuntu/damian/ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py /home/ubuntu/damian/ViTPose/vitpose-b.pth 
 ```
-The first argument is a config file (for ViTpose B)  the second argument is the `.pth` checkpoint (weights). Both can be found on the main site of the repository:
+The first argument is a configuration file (for ViTpose B) and the second argument is the `.pth` checkpoint (weights). Both can be found on the main site of the repository:
+
 <img width="876" alt="image" src="https://user-images.githubusercontent.com/97082108/203548069-7239c758-8332-4d1d-b4d4-94774a4fcdef.png">
 
-The resulting model is about 400mb. 
+The resulting model is about 400 MB. 
 
 ## Benchmarking in DeepSparse:
 
-Naive benchmarking shows that for the dense model, the engine is roughly x2 faster than ORT:
+Naive benchmarking shows that the engine is roughly x2 faster than ORT for the dense model:
+
 <img width="1116" alt="Zrzut ekranu 2022-11-23 o 13 06 23" src="https://user-images.githubusercontent.com/97082108/203562298-3a96c653-58ef-4471-ab4a-faeb222c24b3.png">
 
 ## Postprocessing
-ViT-Pose might be our first candidate for a "composed" deepsparse pipeline. 
-It is a top-down pose estimation approach i.e. we first detect `n` people in the image, and then we estimate their poses individually (using bounding-box-cropped images).
+ViT-Pose might be our first candidate for a "composed" DeepSparse pipeline. 
+As a top-down pose estimation approach, we first detect `n` people in the image and then estimate their poses individually (using bounding box-cropped images).
 We pass the cropped bounding boxes to ViT to get an array `(batch, no_keypoints, h, w)`. To decode this array, according to the original paper, 
 we need some simple composition of transposed convolutions.
 
-What I do naively for now: I  "squash" the array to `(h,w)` and then overlay it on the original image. We can see that the heatmap roughly coincides with the joints of the model.
+If "squash" the array to `(h,w)` and then overlay it on the original image, we can see that the heatmap roughly coincides with the joints of the model.
 
 <img width="585" alt="image" src="https://user-images.githubusercontent.com/97082108/204554128-a12deb08-6f6c-4383-aafc-ea5fee754e0e.png">