updated docs

shoaib42 · Aug 3, 2021 · 171763d · 171763d
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diff --git a/docs/depthnet.md b/docs/depthnet.md
@@ -8,7 +8,7 @@ Depth sensing is useful for tasks such as mapping, navigation and obstacle detec
 
 <img src="https://github.com/dusty-nv/jetson-inference/raw/dev/docs/images/depthnet-0.jpg">
 
-The [`depthNet`]((../c/depthNet.h) object accepts a single color image as input, and outputs the depth map.  The depth map is colorized for visualization, but the raw [depth field](#getting-the-raw-depth-field) is also accessible for directly accessing the depths. [`depthNet`]((../c/depthNet.h) is available to use from [Python](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/python/jetson.inference.html#depthNet) and [C++](../c/depthNet.h).
+The [`depthNet`](../c/depthNet.h) object accepts a single color image as input, and outputs the depth map.  The depth map is colorized for visualization, but the raw [depth field](#getting-the-raw-depth-field) is also accessible for directly accessing the depths. [`depthNet`](../c/depthNet.h) is available to use from [Python](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/python/jetson.inference.html#depthNet) and [C++](../c/depthNet.h).
 
 As examples of using the `depthNet` class, we provide sample programs for C++ and Python:
 

diff --git a/docs/detectnet-console-2.md b/docs/detectnet-console-2.md
@@ -8,7 +8,7 @@ The previous recognition examples output class probabilities representing the en
 
 <img src="https://github.com/dusty-nv/jetson-inference/raw/dev/docs/images/detectnet.jpg" >
 
-The [`detectNet`]((../c/detectNet.h) object accepts an image as input, and outputs a list of coordinates of the detected bounding boxes along with their classes and confidence values.  [`detectNet`]((../c/detectNet.h) is available to use from [Python](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/python/jetson.inference.html#detectNet) and [C++](../c/detectNet.h).  See below for various [pre-trained detection models](#pre-trained-detection-models-available)  available for download.  The default model used is a [91-class](../data/networks/ssd_coco_labels.txt) SSD-Mobilenet-v2 model trained on the MS COCO dataset, which achieves realtime inferencing performance on Jetson with TensorRT. 
+The [`detectNet`](../c/detectNet.h) object accepts an image as input, and outputs a list of coordinates of the detected bounding boxes along with their classes and confidence values.  [`detectNet`](../c/detectNet.h) is available to use from [Python](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/python/jetson.inference.html#detectNet) and [C++](../c/detectNet.h).  See below for various [pre-trained detection models](#pre-trained-detection-models-available)  available for download.  The default model used is a [91-class](../data/networks/ssd_coco_labels.txt) SSD-Mobilenet-v2 model trained on the MS COCO dataset, which achieves realtime inferencing performance on Jetson with TensorRT. 
 
 As examples of using the `detectNet` class, we provide sample programs for C++ and Python:
 

diff --git a/docs/posenet.md b/docs/posenet.md
@@ -8,7 +8,7 @@ Pose estimation consists of locating various body parts (aka keypoints) that for
 
 <img src="https://github.com/dusty-nv/jetson-inference/raw/dev/docs/images/posenet-0.jpg">
 
-The [`poseNet`]((../c/poseNet.h) object accepts an image as input, and outputs a list of object poses.  Each object pose contains a list of detected keypoints, along with their locations and links between keypoints.  You can query these to find particular features.  [`poseNet`]((../c/poseNet.h) can be used from [Python](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/python/jetson.inference.html#poseNet) and [C++](../c/poseNet.h).
+The [`poseNet`](../c/poseNet.h) object accepts an image as input, and outputs a list of object poses.  Each object pose contains a list of detected keypoints, along with their locations and links between keypoints.  You can query these to find particular features.  [`poseNet`](../c/poseNet.h) can be used from [Python](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/python/jetson.inference.html#poseNet) and [C++](../c/poseNet.h).
 
 As examples of using the `poseNet` class, we provide sample programs for C++ and Python:
 

diff --git a/docs/segnet-console-2.md b/docs/segnet-console-2.md
@@ -8,7 +8,7 @@ The next deep learning capability we'll cover in this tutorial is **semantic seg
 
 <img src="https://github.com/dusty-nv/jetson-inference/raw/pytorch/docs/images/segmentation.jpg">
 
-[`segNet`]((../c/segNet.h) accepts as input the 2D image, and outputs a second image with the per-pixel classification mask overlay.  Each pixel of the mask corresponds to the class of object that was classified.  [`segNet`]((../c/segNet.h) is available to use from [Python](https://rawgit.com/dusty-nv/jetson-inference/pytorch/docs/html/python/jetson.inference.html#segNet) and [C++](../c/segNet.h).  
+[`segNet`](../c/segNet.h) accepts as input the 2D image, and outputs a second image with the per-pixel classification mask overlay.  Each pixel of the mask corresponds to the class of object that was classified.  [`segNet`](../c/segNet.h) is available to use from [Python](https://rawgit.com/dusty-nv/jetson-inference/pytorch/docs/html/python/jetson.inference.html#segNet) and [C++](../c/segNet.h).  
 
 As examples of using the `segNet` class, we provide sample programs C++ and Python: