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Signed-off-by: Antonio Martinez <[email protected]>
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@antoniomtz
antoniomtz committed Mar 19, 2024
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144 changes: 111 additions & 33 deletions slides.md
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---
theme: seriph
background: https://source.unsplash.com/collection/94734566/1920x1080
background: ./images/background.jpg
class: text-center
highlighter: shiki
lineNumbers: false
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<li>Questions</li>
</ol>

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You can have `style` tag in markdown to override the style for the current page.
Learn more: https://sli.dev/guide/syntax#embedded-styles
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# Introduction of AI and CV

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# AI and CV Basics

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# Intel Software and Hardware on AI
Expand Down Expand Up @@ -437,23 +420,30 @@ Prerequisites:

Install <a href="https://docs.docker.com/desktop/install/windows-install/" target="_blank">Docker on Windows</a>

<v-click>

## Step 1 Download Moba Xterm

<a href="https://mobaxterm.mobatek.net/download-home-edition.html" target="_blank">MobaXterm</a>
</v-click>
<v-click>

## Step 2 Run DLstreamer docker container

```docker
docker run -it --rm --network=host --entrypoint /bin/bash --name dlstreamer_test --privileged --user root openvino/ubuntu18_data_dev
```
<~br/>
<br/>
</v-click>
<v-click>

## Step 3 Download Models

```bash
cd /opt/intel/openvino/data_processing/dl_streamer/samples
./download_models.sh
```
</v-click>

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```bash
pip install -q "openvino>=2023.1.0"
```
<v-click>

## Initialize openVINO runtime

```python
import openvino as ov
core = ov.Core()
```
</v-click>

<v-click>

## Loading a model

Expand All @@ -508,6 +502,9 @@ classification_model_xml = "model/classification.xml"
model = core.read_model(model=classification_model_xml)
compiled_model = core.compile_model(model=model, device_name="CPU") # Compile model to a specific device
```
</v-click>

<v-click>

## Analyze the input

Expand All @@ -516,22 +513,12 @@ model.inputs # [<Output: names[input, input:0] shape[1,3,224,224] type: f32>]
```

* model expects one input and input data with the batch size of 1 (N), 3 channels ( C) , and images with a height (H) and width (W) equal to 224.

</v-click>
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<style>
.col-left .column:nth-child(1) {
flex: 0 0 70%; /* adjust the percentage value as needed */
}

.col-right .column:nth-child(2) {
flex: 0 0 30%; /* adjust the percentage value as needed */
}
</style>

## Analyze the output

```python
Expand All @@ -540,6 +527,8 @@ model.outputs # [<Output: names[MobilenetV3/Predictions/Softmax] shape[1,1001] t

* shape of [1, 1001] where 1001 is the number of classes

<v-click>

## Load an Image

```python
Expand All @@ -548,6 +537,9 @@ input_image = cv2.resize(src=image, dsize=(224, 224)) # Resize image
input_image = np.expand_dims(input_image, 0) # Reshape image
input_image.shape # (1, 3, 224, 224)
```
</v-click>

<v-click>

## Do Inference
```python
Expand All @@ -556,7 +548,93 @@ result_infer = compiled_model([input_image])[output_layer]
result_index = np.argmax(result_infer)
imagenet_classes[result_index] # n02099267 flat-coated retriever
```
</v-click>

::right::

<img src="/assets/dog.png" />

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# openVINO Jupyter notebooks

<a href="https://github.com/openvinotoolkit/openvino_notebooks" target="_blank">https://github.com/openvinotoolkit/openvino_notebooks</a>

<div grid="~ cols-3 gap-2" m="t-1">
<img src="https://user-images.githubusercontent.com/47499836/163544861-fa2ad64b-77df-4c16-b065-79183e8ed964.png" width="250"/>
<img src="https://user-images.githubusercontent.com/4547501/141471665-82b28c86-cf64-4bfe-98b3-c314658f2d96.gif" width="250"/>
<img src="https://docs.openvino.ai/2024/_images/208-optical-character-recognition-with-output_26_0.png" width="250" />
</div>

<div grid="~ cols-3 gap-2" m="t-1">
<img src="https://docs.openvino.ai/2024/_images/230-yolov8-instance-segmentation-with-output_11_1.png" width="250" />
<img src="https://user-images.githubusercontent.com/91237924/210479548-b70dbbaa-5948-4e49-b48e-6cb6613226da.gif" width="300" />
<img src="https://user-images.githubusercontent.com/10940214/151552326-642d6e49-f5a0-4fc1-bf14-ae3f457e1fec.gif" width="300" />
</div>

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# AI popular trends (genAI)

* Text to Video/Image generation
* Large Language Models (LLM)
* Natural Language procesing (NLP)
* Sound generators

::right::

<table>
<thead>
<tr>
<th style="text-align:center">Darth Vader is surfing on waves</th>
</tr>
</thead>
<tbody>
<tr>
<center>
<td style="text-align:center"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/darthvader_cerpense.gif" width="200" alt=""></td>
</center>
</tr>
</tbody>
</table>

<table>
<thead>
<tr>
<th style="text-align:center">a beautiful pink unicorn</th>
</tr>
</thead>
<tbody>
<tr>
<center>
<td style="text-align:center"><img src="https://user-images.githubusercontent.com/29454499/277367065-13a8f622-8ea7-4d12-b3f8-241d4499305e.png" width="200" alt=""></td>
</center>
</tr>
</tbody>
</table>

---
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# How to start developing AI/CV applications

* Install an IDE (<a href="https://code.visualstudio.com/" target="_blank">vscode</a>)
* Install python or c++, openVINO and Docker
* Demos from openVINO model zoo <a href="https://github.com/openvinotoolkit/open_model_zoo/tree/master/demos" target="_blank">repo</a>
* Retail use cases using openVINO model server gRPC or cAPI and docker containers (<a href="https://github.com/intel-retail/retail-use-cases" target="_blank">link</a>)
<center>
<img src="/assets/vscode.jpg" width="420"/>
</center>


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# Questions
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h1 {
/* h1 {
background-color: #2B90B6;
background-image: linear-gradient(45deg, #4EC5D4 10%, #146b8c 20%);
background-size: 100%;
-webkit-background-clip: text;
-moz-background-clip: text;
-webkit-text-fill-color: transparent;
-moz-text-fill-color: transparent;
}
} */

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