minor updates

Author: ersheng-ai

README.md

@@ -78,12 +78,12 @@ you can use darknet2pytorch to convert it yourself, or download my converted mod
 
 # 2. Inference (Evolving)
 
-## 2.1 416 * 416 Performance on MS COCO dataset (using pretrained DarknetWeights from <https://github.com/AlexeyAB/darknet>)
+## 2.1 Performance on MS COCO dataset (using pretrained DarknetWeights from <https://github.com/AlexeyAB/darknet>)
 
 **ONNX and TensorRT models are converted from Pytorch (TianXiaomo): Pytorch->ONNX->TensorRT.**
 See following sections for more details of conversions.
 
-- val2017 dataset
+- val2017 dataset (416x416)
 
     | Model type          | AP          | AP50        | AP75        |  APS        | APM         | APL         |
     | ------------------- | ----------: | ----------: | ----------: | ----------: | ----------: | ----------: |
@@ -92,7 +92,7 @@ See following sections for more details of conversions.
     | TensorRT FP32 + BatchedNMSPlugin | 0.472| 0.708 |       0.511 |       0.273 |       0.530 |       0.637 |
     | TensorRT FP16 + BatchedNMSPlugin | 0.472| 0.708 |       0.511 |       0.273 |       0.530 |       0.636 |
 
-- testdev2017 dataset
+- testdev2017 dataset (416x416)
 
     | Model type          | AP          | AP50        | AP75        |  APS        | APM         | APL         |
     | ------------------- | ----------: | ----------: | ----------: | ----------: | ----------: | ----------: |
@@ -101,6 +101,7 @@ See following sections for more details of conversions.
     | ONNX                |    incoming |    incoming |    incoming |    incoming |    incoming |    incoming |
     | TensorRT FP32 + BatchedNMSPlugin | 0.412| 0.625 |       0.445 |       0.200 |       0.446 |       0.564 |
 
+
 ## 2.2 Image input size for inference
 
 Image input size is NOT restricted in `320 * 320`, `416 * 416`, `512 * 512` and `608 * 608`.
@@ -132,11 +133,11 @@ Larger input size could help detect smaller targets, but may be slower and GPU m
 
 ## 2.4 Inference output
 
-    There are 2 inference outputs.
-    - One is locations of bounding boxes, its shape is  `[batch, num_boxes, 1, 4]` which represents x1, y1, x2, y2 of each bounding box.
-    - The other one is scores of bounding boxes which is of shape `[batch, num_boxes, num_classes]` indicating scores of all classes for each bounding box.
+There are 2 inference outputs.
+- One is locations of bounding boxes, its shape is  `[batch, num_boxes, 1, 4]` which represents x1, y1, x2, y2 of each bounding box.
+- The other one is scores of bounding boxes which is of shape `[batch, num_boxes, num_classes]` indicating scores of all classes for each bounding box.
 
-    Until now, still a small piece of post-processing including NMS is required. We are trying to minimize time and complexity of post-processing.
+Until now, still a small piece of post-processing including NMS is required. We are trying to minimize time and complexity of post-processing.
 
 
 # 3. Darknet2ONNX (Evolving)