Minor fixes in bug-report and README (#180)

* Update README.md

* Minor fixes in bug-report and README.md

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -12,7 +12,7 @@ body:
     description: |
       Please provide a clear and concise description of what the bug is.
 
-      If relevant, add a minimal example so that we can reproduce the error by running the code. It is very important for he snippet to be as succinct (minimal) as possible, so please take time to trim down any irrelevant code to help us debug efficiently. We are going to copy-paste your code and we expect to get the same result as you did: avoid any external data, and include the relevant imports, etc. For example:
+      If relevant, add a minimal example so that we can reproduce the error by running the code. It is very important for the snippet to be as succinct (minimal) as possible, so please take time to trim down any irrelevant code to help us debug efficiently. We are going to copy-paste your code and we expect to get the same result as you did: avoid any external data, and include the relevant imports, etc. For example:
 
       ```python
       # All necessary imports at the beginning

README.md

@@ -112,12 +112,9 @@ The following is the interface for loading the checkpoint weights trained with `
 ```python
 from yolort.models import YOLOv5
 
-# Model
-yolov5 = YOLOv5()
-
 # 'yolov5s.pt' is downloaded from https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt
 ckpt_path_from_ultralytics = 'yolov5s.pt'
-model = yolov5.load_from_yolov5(ckpt_path_from_ultralytics, score_thresh=0.25)
+model = YOLOv5.load_from_yolov5(ckpt_path_from_ultralytics, score_thresh=0.25)
 
 model.eval()
 img_path = 'test/assets/bus.jpg'
@@ -132,6 +129,8 @@ We provide a [notebook](notebooks/inference-pytorch-export-libtorch.ipynb) to de
 
 ### Inference on `ONNXRuntime` backend
 
+TBD
+
 ## 🎨 Model Graph Visualization
 
 Now, `yolort` can draw the model graph directly, checkout our [model-graph-visualization](notebooks/model-graph-visualization.ipynb) notebook to see how to use and visualize the model graph.