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rm python object_detection_sample_ssd (#8880)

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* remove python object_detection_sample_ssd

* rm refs to deleted python sample
This commit is contained in:
Vladimir Dudnik
2021-12-02 09:51:36 +03:00
committed by GitHub
parent 4b8d6c59e3
commit bc8fbf530b
10 changed files with 16 additions and 313 deletions
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1
docs/IE_DG/Samples_Overview.md
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@@ -40,7 +40,6 @@ Inference Engine sample applications include the following:
- **Object Detection for SSD Sample** – Inference of object detection networks based on the SSD, this sample is simplified version that supports only images as inputs.
- [Object Detection SSD C++ Sample](../../samples/cpp/object_detection_sample_ssd/README.md)
- [Object Detection SSD C Sample](../../samples/c/object_detection_sample_ssd/README.md)
- [Object Detection SSD Python* Sample](../../samples/python/object_detection_sample_ssd/README.md)
> **NOTE**: All C++ samples support input paths containing only ASCII characters, except the Hello Classification Sample, that supports Unicode.

2
docs/IE_DG/supported_plugins/HETERO.md
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@@ -49,7 +49,7 @@ Intermediate blobs between these sub graphs are allocated automatically in the m
Samples can be used with the following command:
```sh
./object_detection_sample_ssd -m <path_to_model>/ModelSSD.xml -i <path_to_pictures>/picture.jpg -d HETERO:GPU,CPU
./hello_classification <path_to_model>/squeezenet1.1.xml <path_to_pictures>/picture.jpg HETERO:GPU,CPU
```
where:
- `HETERO` stands for heterogeneous plugin

1
docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_EfficientDet_Models.md
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@@ -69,7 +69,6 @@ The attribute names are self-explanatory or match the name in the `hparams_confi
OpenVINO&trade; toolkit provides samples that can be used to infer EfficientDet model. For more information, refer to
[Object Detection for SSD C++ Sample](@ref openvino_inference_engine_samples_object_detection_sample_ssd_README) and
[Object Detection for SSD Python Sample](@ref openvino_inference_engine_ie_bridges_python_sample_object_detection_sample_ssd_README).
## <a name="efficientdet-ir-results-interpretation"></a>Interpreting Results of the TensorFlow Model and the IR

1
docs/doxygen/openvino_docs.xml
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@@ -174,7 +174,6 @@ limitations under the License.
<tab type="user" title="nGraph Function Creation C++ Sample" url="@ref openvino_inference_engine_samples_ngraph_function_creation_sample_README"/>
<tab type="user" title="nGraph Function Creation Python* Sample" url="@ref openvino_inference_engine_ie_bridges_python_sample_ngraph_function_creation_sample_README"/>
<tab type="user" title="Object Detection SSD C++ Sample" url="@ref openvino_inference_engine_samples_object_detection_sample_ssd_README"/>
<tab type="user" title="Object Detection SSD Python* Sample" url="@ref openvino_inference_engine_ie_bridges_python_sample_object_detection_sample_ssd_README"/>
<tab type="user" title="Object Detection SSD C Sample" url="@ref openvino_inference_engine_ie_bridges_c_samples_object_detection_sample_ssd_README"/>
<tab type="user" title="Automatic Speech Recognition C++ Sample" url="@ref openvino_inference_engine_samples_speech_sample_README"/>
<tab type="user" title="Automatic Speech Recognition Python Sample" url="@ref openvino_inference_engine_ie_bridges_python_sample_speech_sample_README"/>

14
docs/get_started/get_started_raspbian.md
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@@ -54,29 +54,29 @@ The OpenVINO™ workflow on Raspbian* OS is as follows:
## <a name="using-sample"></a>Build and Run Code Samples
Follow the steps below to run pre-trained Face Detection network using Inference Engine samples from the OpenVINO toolkit.
Follow the steps below to run pre-trained SqueezeNet image classification network using Inference Engine samples from the OpenVINO toolkit.
1. Create a samples build directory. This example uses a directory named `build`:
```sh
mkdir build && cd build
```
2. Build the Object Detection Sample with the following command:
2. Build the Hello Classification Sample with the following command:
```sh
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-march=armv7-a" /opt/intel/openvino_2022/samples/cpp
make -j2 object_detection_sample_ssd
make -j2 hello_classification
```
3. Download the pre-trained Face Detection model with the [Model Downloader tool](@ref omz_tools_downloader):
3. Download the pre-trained SqueezeNet image classification model with the [Model Downloader tool](@ref omz_tools_downloader):
```sh
git clone --depth 1 https://github.com/openvinotoolkit/open_model_zoo
cd open_model_zoo/tools/downloader
python3 -m pip install -r requirements.in
python3 downloader.py --name face-detection-adas-0001
python3 downloader.py --name squeezenet1.1
```
4. Run the sample, specifying the model and path to the input image:
```sh
./armv7l/Release/object_detection_sample_ssd -m face-detection-adas-0001.xml -d MYRIAD -i <path_to_image>
./armv7l/Release/hello_classification <path_to_model>/squeezenet1.1.xml <path_to_image> MYRIAD
```
The application outputs an image (`out_0.bmp`) with detected faced enclosed in rectangles.
The application outputs to console window top 10 classification results.
## <a name="basic-guidelines-sample-application"></a>Basic Guidelines for Using Code Samples

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docs/install_guides/installing-openvino-raspbian.md
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@@ -138,25 +138,25 @@ Follow the next steps to use the pre-trained face detection model using Inferenc
```sh
mkdir build && cd build
```
2. Build the Object Detection Sample:
2. Build the Hello Classification Sample:
```sh
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-march=armv7-a" /opt/intel/openvino_2022/samples/cpp
```
```sh
make -j2 object_detection_sample_ssd
make -j2 hello_classifiaction
```
3. Download the pre-trained Face Detection model with the Model Downloader or copy it from the host machine:
3. Download the pre-trained squeezenet1.1 image classifiaction model with the Model Downloader or copy it from the host machine:
```sh
git clone --depth 1 https://github.com/openvinotoolkit/open_model_zoo
cd open_model_zoo/tools/downloader
python3 -m pip install -r requirements.in
python3 downloader.py --name face-detection-adas-0001
python3 downloader.py --name squeezenet1.1
```
4. Run the sample specifying the model, a path to the input image, and the VPU required to run with the Raspbian* OS:
```sh
./armv7l/Release/object_detection_sample_ssd -m <path_to_model>/face-detection-adas-0001.xml -d MYRIAD -i <path_to_image>
./armv7l/Release/hello_classification <path_to_model>/squeezenet1.1.xml <path_to_image> MYRIAD
```
The application outputs an image (`out_0.bmp`) with detected faced enclosed in rectangles.
The application outputs to console window top 10 classification results.
Congratulations, you have finished the OpenVINO™ toolkit for Raspbian* OS installation. You have completed all required installation, configuration and build steps in this guide.