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DOCS: Fixing broken links in documentation. (#14935)

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This commit is contained in:
Sebastian Golebiewski
2023-01-05 08:25:03 +01:00
committed by GitHub
parent 0d261dbf83
commit 3017c8d123
16 changed files with 20 additions and 21 deletions
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tools/legacy/benchmark_app/README.md
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@@ -53,7 +53,7 @@ Note that the benchmark_app usually produces optimal performance for any device
./benchmark_app -m <model> -i <input> -d CPU
```
It still may be sub-optimal for some cases, especially for very small networks. For all devices, including the [MULTI device](../../../docs/OV_Runtime_UG/supported_plugins/MULTI.md) it is preferable to use the FP16 IR for the model. If latency of the CPU inference on the multi-socket machines is of concern.
It still may be sub-optimal for some cases, especially for very small networks. For all devices, including the [MULTI device](../../../docs/OV_Runtime_UG/multi_device.md) it is preferable to use the FP16 IR for the model. If latency of the CPU inference on the multi-socket machines is of concern.
These, as well as other topics are explained in the [Performance Optimization Guide](../../../docs/optimization_guide/dldt_deployment_optimization_guide.md).
Running the application with the `-h` option yields the following usage message:

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tools/pot/openvino/tools/pot/api/samples/3d_segmentation/README.md
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# Quantizatiing 3D Segmentation Model {#pot_example_3d_segmentation_README}
This example demonstrates the use of the [Post-training Optimization Tool API](@ref pot_compression_api_README) for the task of quantizing a 3D segmentation model.
The [Brain Tumor Segmentation](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/brain-tumor-segmentation-0002/brain-tumor-segmentation-0002.md) model from PyTorch* is used for this purpose.
The [Brain Tumor Segmentation](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/brain-tumor-segmentation-0002) model from PyTorch* is used for this purpose.
A custom `DataLoader` is created to load images in NIfTI format from [Medical Segmentation Decathlon BRATS 2017](http://medicaldecathlon.com/) dataset for 3D semantic segmentation task
and the implementation of Dice Index metric is used for the model evaluation. In addition, this example demonstrates how one can use image metadata obtained during image reading and
preprocessing to post-process the model raw output. The code of the example is available on [GitHub](https://github.com/openvinotoolkit/openvino/tree/master/tools/pot/openvino/tools/pot/api/samples/3d_segmentation).

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tools/pot/openvino/tools/pot/api/samples/classification/README.md
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# Quantizing Image Classification Model {#pot_example_classification_README}
This example demonstrates the use of the [Post-training Optimization Tool API](@ref pot_compression_api_README) for the task of quantizing a classification model.
The [MobilenetV2](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/mobilenet-v2-1.0-224/mobilenet-v2-1.0-224.md) model from TensorFlow* is used for this purpose.
The [MobilenetV2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-v2-1.0-224) model from TensorFlow* is used for this purpose.
A custom `DataLoader` is created to load the [ImageNet](http://www.image-net.org/) classification dataset and the implementation of Accuracy at top-1 metric is used for the model evaluation. The code of the example is available on [GitHub](https://github.com/openvinotoolkit/openvino/tree/master/tools/pot/openvino/tools/pot/api/samples/classification).
## How to prepare the data

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tools/pot/openvino/tools/pot/api/samples/face_detection/README.md
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# Quantizing Cascaded Face detection Model {#pot_example_face_detection_README}
This example demonstrates the use of the [Post-training Optimization Tool API](@ref pot_compression_api_README) for the task of quantizing a face detection model.
The [MTCNN](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/mtcnn/mtcnn.md) model from Caffe* is used for this purpose.
The [MTCNN](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mtcnn) model from Caffe* is used for this purpose.
A custom `DataLoader` is created to load [WIDER FACE](http://shuoyang1213.me/WIDERFACE/) dataset for a face detection task
and the implementation of Recall metric is used for the model evaluation. In addition, this example demonstrates how one can implement
an engine to infer a cascaded (composite) model that is represented by multiple submodels in an OpenVino&trade; Intermediate Representation (IR)

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tools/pot/openvino/tools/pot/api/samples/segmentation/README.md
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# Quantizing Semantic Segmentation Model {#pot_example_segmentation_README}
This example demonstrates the use of the [Post-training Optimization Tool API](@ref pot_compression_api_README) for the task of quantizing a segmentation model.
The [DeepLabV3](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/deeplabv3/deeplabv3.md) model from TensorFlow* is used for this purpose.
The [DeepLabV3](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/deeplabv3) model from TensorFlow* is used for this purpose.
A custom `DataLoader` is created to load the [Pascal VOC 2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/) dataset for semantic segmentation task
and the implementation of Mean Intersection Over Union metric is used for the model evaluation. The code of the example is available on [GitHub](https://github.com/openvinotoolkit/openvino/tree/master/tools/pot/openvino/tools/pot/api/samples/segmentation).