GstInference/Example Applications/Classification: Difference between revisions

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 This example receives an input video file and classifies each frame into one of 1000 possible classes. For each classified frame the application captures the signal emitted by GstInference, forwarding the prediction to a placeholder for external logic. Simultaneously, the pipeline displays the captured frames with the associated label in a window. Note that the image currently being displayed '''not necessarily''' matches the one being handled by the signal. The display is meant for visualization and debugging purposes.
-The classification architecture being used by the example is InceptionV4 trained using the [http://image-net.org/challenges/LSVRC/2012/browse-synsets Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) Dataset]. A pre-trained model can be downloaded from the [https://www.ridgerun.com/store/Deep-Learning-Models-and-Binaries-c33344794 GstInference Model Zoo]
+The classification architecture being used by the example is InceptionV4 trained using the [http://image-net.org/challenges/LSVRC/2012/browse-synsets Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) Dataset]. A pre-trained model can be downloaded from the [https://developer.ridgerun.com/wiki/index.php?title=GstInference/Model_Zoo GstInference Model Zoo]
 This examples serves both as an example and as a starting point for a classification application.