Replace some URLs that redirect with the ones they redirect to

This makes them quicker to open and reduces the chance of link rot.

README.md

@@ -43,7 +43,7 @@ Open Model Zoo is licensed under Apache License, Version 2.0. By contributing to
 Please report questions, issues and suggestions using:
 * [\#open_model_zoo](https://stackoverflow.com/search?q=%23open_model_zoo) tag on StackOverflow*
 * [GitHub* Issues](https://github.com/opencv/open_model_zoo/issues)
-* [Forum](https://software.intel.com/en-us/forums/computer-vision)
+* [Forum](https://software.intel.com/en-us/forums/intel-distribution-of-openvino-toolkit)
 * [Gitter](https://gitter.im/open_model_zoo/community)
 
 ---

demos/README.md

@@ -113,7 +113,7 @@ You can also build demos manually using Inference Engine binaries from the
 [dldt](https://github.com/opencv/dldt/tree/master) repo. In this case please set `InferenceEngine_DIR` to a CMake folder you built the dldt project from, for example `<dldt_repo>/inference-engine/build`.
 Please also set the `OpenCV_DIR` variable pointing to the required OpenCV package. The same OpenCV
 version should be used both for the inference engine and demos build.
-Please refer to the Inference Engine [build instructions](https://github.com/opencv/dldt/tree/master/inference-engine/README.md)
+Please refer to the Inference Engine [build instructions](https://github.com/opencv/dldt/blob/master/inference-engine/README.md)
 for details. Please also add path to built Inference Engine libraries to `LD_LIBRARY_PATH` (Linux*) or `PATH` (Windows*) variable before building the demos.
 
 ### <a name="build_demos_linux"></a>Build the Demo Applications on Linux*

models/intel/action-recognition-0001-decoder/description/action-recognition-0001-decoder.md

@@ -3,7 +3,7 @@
 ## Use Case and High-Level Description
 
 This is a general-purpose action recognition model for Kinetics-400 dataset. The model uses Video Transformer approach with ResNet34 encoder.
-Please refer to the [kinetics](https://deepmind.com/research/open-source/open-source-datasets/kinetics/) dataset specification to see list of action that are recognised by this model.
+Please refer to the [kinetics](https://deepmind.com/research/open-source/kinetics) dataset specification to see list of action that are recognised by this model.
 
 This model is only decoder part of the whole pipeline. It accepts stack of frame embeddings, computed by action-recognition-0001-encoder, and produces prediction on input video. Video frames should be sampled to cover ~1 second fragment (i.e. skip every second frame in 30 fps video).
 

models/intel/action-recognition-0001-encoder/description/action-recognition-0001-encoder.md

@@ -3,7 +3,7 @@
 ## Use Case and High-Level Description
 
 This is a general-purpose action recognition model for Kinetics-400 dataset. The model uses Video Transformer approach with ResNet34 encoder.
-Please refer to the [kinetics](https://deepmind.com/research/open-source/open-source-datasets/kinetics/) dataset specification to see list of action that are recognised by this model.
+Please refer to the [kinetics](https://deepmind.com/research/open-source/kinetics) dataset specification to see list of action that are recognised by this model.
 
 This model is only encoder part of the whole pipeline. It accepts video frame and produces embedding.
 Use action-recognition-0001-decoder to produce prediction from embeddings of 16 frames.

models/public/ctdet_coco_dlav0_384/ctdet_coco_dlav0_384.md

@@ -24,7 +24,7 @@ git checkout 8ef87b4
 git apply /path/to/pytorch-onnx.patch
 ```
 4. Follow the original [installation steps](https://github.com/xingyizhou/CenterNet/blob/8ef87b4/readme/INSTALL.md)
-5. Download the [pretrained weights](https://drive.google.com/open?id=18yBxWOlhTo32_swSug_HM4q3BeWgxp_N)
+5. Download the [pretrained weights](https://drive.google.com/file/d/18yBxWOlhTo32_swSug_HM4q3BeWgxp_N/view)
 6. Run
 ```sh
 python convert.py ctdet --load_model /path/to/downloaded/weights.pth --exp_id coco_dlav0_384 --arch dlav0_34 --input_res 384 --gpus -1

models/public/ctdet_coco_dlav0_512/ctdet_coco_dlav0_512.md

@@ -24,7 +24,7 @@ git checkout 8ef87b4
 git apply /path/to/pytorch-onnx.patch
 ```
 4. Follow the original [installation steps](https://github.com/xingyizhou/CenterNet/blob/8ef87b4/readme/INSTALL.md)
-5. Download the [pretrained weights](https://drive.google.com/open?id=18yBxWOlhTo32_swSug_HM4q3BeWgxp_N)
+5. Download the [pretrained weights](https://drive.google.com/file/d/18yBxWOlhTo32_swSug_HM4q3BeWgxp_N/view)
 6. Run
 ```sh
 python convert.py ctdet --load_model /path/to/downloaded/weights.pth --exp_id coco_dlav0_512 --arch dlav0_34 --input_res 512 --gpus -1

models/public/squeezenet1.0/model.yml

@@ -16,7 +16,7 @@ description: >-
   The "squeezenet1.0" model is one of the SqueezeNet <https://arxiv.org/pdf/1602.07360>
   topology models, is designed to perform image classification. The SqueezeNet models
   have been pre-trained on the ImageNet image database. For details about this family
-  of models, check out the repository <https://github.com/DeepScale/SqueezeNet>.
+  of models, check out the repository <https://github.com/forresti/SqueezeNet>.
 
   The model input is a blob that consists of a single image of 1x3x227x227 in BGR
   order. The BGR mean values need to be subtracted as follows: [104, 117, 123] before
@@ -29,11 +29,11 @@ files:
   - name: squeezenet1.0.prototxt
     size: 9640
     sha256: 6e4ecef2a27347e226a5ef8be31d6d1b9d19f5a40afa1986ec259fd5fa3bd91c
-    source: https://raw.githubusercontent.com/DeepScale/SqueezeNet/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.0/deploy.prototxt
+    source: https://raw.githubusercontent.com/forresti/SqueezeNet/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.0/deploy.prototxt
   - name: squeezenet1.0.caffemodel
     size: 5001403
     sha256: 9ff8035aada1f9ffa880b35252680d971434b141ec9fbacbe88309f0f9a675ce
-    source: https://github.com/DeepScale/SqueezeNet/raw/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.0/squeezenet_v1.0.caffemodel
+    source: https://github.com/forresti/SqueezeNet/raw/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.0/squeezenet_v1.0.caffemodel
 postprocessing:
   - $type: regex_replace
     file: squeezenet1.0.prototxt
@@ -47,4 +47,4 @@ model_optimizer_args:
   - --input_model=$dl_dir/squeezenet1.0.caffemodel
   - --input_proto=$dl_dir/squeezenet1.0.prototxt
 framework: caffe
-license: https://raw.githubusercontent.com/DeepScale/SqueezeNet/master/LICENSE
+license: https://raw.githubusercontent.com/forresti/SqueezeNet/master/LICENSE

models/public/squeezenet1.0/squeezenet1.0.md

@@ -2,7 +2,7 @@
 
 ## Use Case and High-Level Description
 
-The `squeezenet1.0` model is one of the [SqueezeNet](https://arxiv.org/pdf/1602.07360) topology models, is designed to perform image classification. The SqueezeNet models have been pre-trained on the ImageNet image database. For details about this family of models, check out the [repository](https://github.com/DeepScale/SqueezeNet).
+The `squeezenet1.0` model is one of the [SqueezeNet](https://arxiv.org/pdf/1602.07360) topology models, is designed to perform image classification. The SqueezeNet models have been pre-trained on the ImageNet image database. For details about this family of models, check out the [repository](https://github.com/forresti/SqueezeNet).
 
 The model input is a blob that consists of a single image of 1x3x227x227 in BGR order. The BGR mean values need to be subtracted as follows: [104, 117, 123] before passing the image blob into the network.
 
@@ -67,7 +67,7 @@ Object classifier according to ImageNet classes, name - `prob`, shape - `1,1000`
 ## Legal Information
 
 The original model is distributed under the following
-[license](https://raw.githubusercontent.com/DeepScale/SqueezeNet/master/LICENSE):
+[license](https://raw.githubusercontent.com/forresti/SqueezeNet/master/LICENSE):
 
 ```
 BSD LICENSE.

models/public/squeezenet1.1/model.yml

@@ -17,7 +17,7 @@ description: >-
   topology. It is designed to perform image classification.  It requires 2.4x less
   computation than SqueezeNet v1.0 <../squeezenet1.0/squeezenet1.0.md> without diminishing
   accuracy. The SqueezeNet models have been pre-trained on the ImageNet image database.
-  For details about this family of models, check out the repository <https://github.com/DeepScale/SqueezeNet>.
+  For details about this family of models, check out the repository <https://github.com/forresti/SqueezeNet>.
 
   The model input is a blob that consists of a single image of 1x3x227x227 in BGR
   order. The BGR mean values need to be subtracted as follows: [104, 117, 123] before
@@ -30,11 +30,11 @@ files:
   - name: squeezenet1.1.prototxt
     size: 9678
     sha256: d041bfb2ab4b32fda4ff6c6966684132f2924e329916aa5bfe9285c6b23e3d1c
-    source: https://raw.githubusercontent.com/DeepScale/SqueezeNet/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.1/deploy.prototxt
+    source: https://raw.githubusercontent.com/forresti/SqueezeNet/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.1/deploy.prototxt
   - name: squeezenet1.1.caffemodel
     size: 4950080
     sha256: 72b912ace512e8621f8ff168a7d72af55910d3c7c9445af8dfbff4c2ee960142
-    source: https://github.com/DeepScale/SqueezeNet/raw/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.1/squeezenet_v1.1.caffemodel
+    source: https://github.com/forresti/SqueezeNet/raw/a47b6f13d30985279789d08053d37013d67d131b/SqueezeNet_v1.1/squeezenet_v1.1.caffemodel
 postprocessing:
   - $type: regex_replace
     file: squeezenet1.1.prototxt
@@ -48,4 +48,4 @@ model_optimizer_args:
   - --input_model=$dl_dir/squeezenet1.1.caffemodel
   - --input_proto=$dl_dir/squeezenet1.1.prototxt
 framework: caffe
-license: https://raw.githubusercontent.com/DeepScale/SqueezeNet/master/LICENSE
+license: https://raw.githubusercontent.com/forresti/SqueezeNet/master/LICENSE

models/public/squeezenet1.1/squeezenet1.1.md

@@ -2,7 +2,7 @@
 
 ## Use Case and High-Level Description
 
-The `squeezenet1.1` updated version of the [SqueezeNet](https://arxiv.org/pdf/1602.07360) topology. It is designed to perform image classification.  It requires 2.4x less computation than [SqueezeNet v1.0](../squeezenet1.0/squeezenet1.0.md) without diminishing accuracy. The SqueezeNet models have been pre-trained on the ImageNet image database. For details about this family of models, check out the [repository](https://github.com/DeepScale/SqueezeNet).
+The `squeezenet1.1` updated version of the [SqueezeNet](https://arxiv.org/pdf/1602.07360) topology. It is designed to perform image classification.  It requires 2.4x less computation than [SqueezeNet v1.0](../squeezenet1.0/squeezenet1.0.md) without diminishing accuracy. The SqueezeNet models have been pre-trained on the ImageNet image database. For details about this family of models, check out the [repository](https://github.com/forresti/SqueezeNet).
 
 The model input is a blob that consists of a single image of 1x3x227x227 in BGR order. The BGR mean values need to be subtracted as follows: [104, 117, 123] before passing the image blob into the network.
 
@@ -67,7 +67,7 @@ Object classifier according to ImageNet classes, name - `prob`, shape - `1,1000`
 ## Legal Information
 
 The original model is distributed under the following
-[license](https://raw.githubusercontent.com/DeepScale/SqueezeNet/master/LICENSE):
+[license](https://raw.githubusercontent.com/forresti/SqueezeNet/master/LICENSE):
 
 ```
 BSD LICENSE.

tools/accuracy_checker/README.md

@@ -10,7 +10,7 @@ Install prerequisites first:
 
 **accuracy checker** uses **Python 3**. Install it first:
 
-- [Python3](https://www.python.org/downloads/), [setuptools](https://pypi.python.org/pypi/setuptools):
+- [Python3](https://www.python.org/downloads/), [setuptools](https://pypi.org/project/setuptools/):
 
 ```bash
 sudo apt-get install python3 python3-dev python3-setuptools python3-pip