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update README according to Issue #1
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README.md

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@@ -69,20 +69,25 @@ can be used as input to my FaceNet TensorRT implementation.
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You will need all models from the repo in the [mtCNNModels](./mtCNNModels) folder so please do this
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to download them:
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```bash
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cd path/to/project/mtCNNModels
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wget https://github.com/PKUZHOU/MTCNN_FaceDetection_TensorRT/blob/master/det1_relu.caffemodel
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wget https://github.com/PKUZHOU/MTCNN_FaceDetection_TensorRT/blob/master/det1_relu.prototxt
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wget https://github.com/PKUZHOU/MTCNN_FaceDetection_TensorRT/blob/master/det2_relu.caffemodel
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wget https://github.com/PKUZHOU/MTCNN_FaceDetection_TensorRT/blob/master/det2_relu.prototxt
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wget https://github.com/PKUZHOU/MTCNN_FaceDetection_TensorRT/blob/master/det3_relu.caffemodel
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wget https://github.com/PKUZHOU/MTCNN_FaceDetection_TensorRT/blob/master/det3_relu.prototxt
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# go to one above project,
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cd path/to/project/..
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# clone PKUZHOUs repo,
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git clone https://github.com/PKUZHOU/MTCNN_FaceDetection_TensorRT
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# and move models into mtCNNModels folder
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mv MTCNN_FaceDetection_TensorRT/det* path/to/project/mtCNNModels
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```
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After doing so you should have the following files in your [mtCNNModels](./mtCNNModels) folder:<br>
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* det1_relu.caffemodel
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* det1_relu.prototxt
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* det2_relu.caffemodel
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* det2_relu.prototxt
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* det3_relu.caffemodel
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* det3_relu.prototxt
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* README.md
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Done you are ready to build the project!
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#### 5. Build the project
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_NOTE:_ This step might take a while when done the first time. TensorRT
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now parses and serializes the model from .uff to a runtime engine
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(.engine file).
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```bash
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mkdir build && cd build
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cmake -DCMAKE_BUILD_TYPE=Release ..
@@ -106,6 +111,10 @@ you have opened your terminal and put in the name of the person you want to add.
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```
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Press "**Q**" to quit and to show the stats (fps).
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_NOTE:_ This step might take a while when done the first time. TensorRT
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now parses and serializes the model from .uff to a runtime engine
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(.engine file).
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## Performance
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Performance on **NVIDIA Jetson Nano**
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* ~60ms +/- 20ms for face detection using mtCNN
@@ -125,4 +134,4 @@ were trained on.
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## Info
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Niclas Wesemann <br>
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