https://github.com/tlesort/yolo-tensorrt-gie-

This code is an implementation of a trained YOLO neural network used with the TensorRT framework.
https://github.com/tlesort/yolo-tensorrt-gie-

tensorrt yolov1

Last synced: 3 months ago
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This code is an implementation of a trained YOLO neural network used with the TensorRT framework.

• Host: GitHub
• URL: https://github.com/tlesort/yolo-tensorrt-gie-
• Owner: TLESORT
• Created: 2017-01-27T13:46:06.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2017-02-22T09:44:57.000Z (over 8 years ago)
• Last Synced: 2025-04-01T23:07:57.228Z (7 months ago)
• Topics: tensorrt, yolov1
• Language: C++
• Homepage:
• Size: 173 KB
• Stars: 88
• Watchers: 7
• Forks: 25
• Open Issues: 12
• Metadata Files:
  • Readme: README.md

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README

          
# Project YOLO-TensorRT-GIE

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 : 

![Data example](Images/cat_detection.jpg)


*Example of bad detection for a cat*

When the ouputed result with the very same network implemented with caffe gives : 

![Data example](Images/true_detection.jpg)


*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. 

![Data example](Images/zeros_detection.jpg)


*Outputed detection for a matrice of zeros*

# UPDATE :

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.

![Data example](Images/cat_detection_modified_32bits.jpg)


*Example of the 32 bits detection with modified prototxt*

But the result of the 16 bits version of tensorRT does not gives correct detections :

![Data example](Images/cat_detection_modified_16bits.jpg)


*Example of the 16 bits detection with modified prototxt*

![Data example](Images/zeros_detection_modified_16bits.jpg)


*Example of the 16 bits detection with modified prototxt with a matrice of zeros*