fix: remove redundant blanks

docs/Test_speed.md

@@ -16,25 +16,25 @@ Here, we use Torch1.8.0 inference on V100 and TensorRT 7.2 Cuda 10.2 Cudnn 8.0.2
 
 #### 2.1 Torch Inference on V100
 
-To get inference speed without TensorRT on V100,  you can run the following command:
+To get inference speed without TensorRT on V100, you can run the following command:
 
 ```shell
-python tools/eval.py --data data/coco.yaml  --batch 32 --weights yolov6n.pt --task speed [--half]
+python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6n.pt --task speed [--half]
 ```
 
 - Speed results with batchsize = 1 are unstable in multiple runs, thus we do not provide the bs1 speed results.
 
 #### 2.2 TensorRT Inference on T4
 
-To  get inference speed with TensorRT in FP16 mode on T4, you can follow the steps below:
+To get inference speed with TensorRT in FP16 mode on T4, you can follow the steps below:
 
-First, export pytorch model as onnx format using the  following command:
+First, export pytorch model as onnx format using the following command:
 
 ```shell
 python deploy/ONNX/export_onnx.py --weights yolov6n.pt --device 0 --batch [1 or 32]
 ```
 
-Second,  generate an inference  trt engine and test speed using `trtexec`:
+Second, generate an inference trt engine and test speed using `trtexec`:
 
 ```
 trtexec --onnx=yolov6n.onnx --workspace=1024 --avgRuns=1000 --inputIOFormats=fp16:chw --outputIOFormats=fp16:chw