This code is an implementation of trained YOLO neural network used with the TensorRT framework. (YOLO : "You Only Look Once: Unified, Real-Time Object Detection" by Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi).
There is issue with this implementation : for now the output of the neural network isn't good and I am currently searching for the solution if you have any idea don't hesitate to create an issue.
The ouputed result for detecting a cat is :
Example of bad detection for a cat
When the ouputed result with the very same network implemented with caffe gives :
True detection for a cat when the network is run with the caffe framework
Furthermore a different images will gives very close results. For example with a matrice of zeros the result seems to be exactly the same.
Outputed detection for a matrice of zeros
As pointed out by AastaLLL at https://devtalk.nvidia.com/default/topic/990426/jetson-tx1/tensorrt-yolo-inference-error/post/5087820/ the leaky relu layer was not supported by TensorRT and should be remplaced by standard-relu+scale+eltwise to approximate it. The results with the modified prototxt (yolo_small_modified.prototxt) are the following :
The 32 bits versions of tensorRT gives similar results to caffe results with yolov1-small.
Example of the 32 bits detection with modified prototxt
But the result of the 16 bits version of tensorRT does not gives correct detections :
Example of the 16 bits detection with modified prototxt
Example of the 16 bits detection with modified prototxt with a matrice of zeros
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