Difference between revisions of "GstInference/Example Applications"
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;[[GstInference/Example Applications/Detection|Detection]] | ;[[GstInference/Example Applications/Detection|Detection]] | ||
:An example using TinyYoloV2 that receives an input video file and detects objects in each buffer. There are 20 different possible objects to detect. | :An example using TinyYoloV2 that receives an input video file and detects objects in each buffer. There are 20 different possible objects to detect. | ||
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;[[GstInference/Example Applications/DispTec|DispTec]] | ;[[GstInference/Example Applications/DispTec|DispTec]] | ||
:A quick and easy guide to get a simple GstInference example running on either a computer running Ubuntu 18.04 or a NVIDIA Jetson TX2. | :A quick and easy guide to get a simple GstInference example running on either a computer running Ubuntu 18.04 or a NVIDIA Jetson TX2. |
Latest revision as of 17:43, 5 April 2021
Make sure you also check GstInference's companion project: R2Inference |
This section provides a series of applications that exemplify how to use GstInference in an application. They also serve as placeholders for extending the prediction with custom logic.
- Classification
- An example using InceptionV4 to classify the frames from a video file in one of the 1000 possible classes. The example provides a placeholder for external code.
- Detection
- An example using TinyYoloV2 that receives an input video file and detects objects in each buffer. There are 20 different possible objects to detect.
- DispTec
- A quick and easy guide to get a simple GstInference example running on either a computer running Ubuntu 18.04 or a NVIDIA Jetson TX2.