Training Object Detection with pretrained resnet 18 , following Detectnet_V2 example

Hello ,
I just trained object detection for 2 classes. After finishing training i wanted to run the detect_net_inference_imagefeeder app. I modified the detect_net_inference.subgraph.json file, putting in a second label under detection decoder.
But when i try to run the app i get error message:

Where can i set the number of classes to detect ? I can’t find any file where i can edit the number of classes.

Thanks in advance!

Hi markus,
Could you refer to
It will run inference with Deepstream SDK.

User can edit the number of classes in Deepstream config file .
For example,


Hi Morganh, thanks for the quick reply. Currently facing another problem, when running the evaluating step in the Jupyter Notebook example. At there is described to add the lines to the detectnet_v2_train_resnet18_kitti.txt file like this
validation_data_source: {
tfrecords_path: “path to testing tfrecords root”
image_directory_path: “”
But i dont have a “path to testing tfrecords root”. Or is there a way to generate those files ?

Please see
You can use parts of training tfrecords for validation.

validation_fold: 0

Or set as below

validation_data_source: {
tfrecords_path: “path to testing tfrecords root”
image_directory_path: “”

That means if you have a new tfrecord, you can set it for validation.

Thanks. I got another question: When i visualize inferences in the Jupyter Notebook with tlt-infer on the testdata for the object, i get quite accurate bounding boxes on the object. However if i export the .tlt model and use the detect_net_inference_imagefeeder app i get completly different bounding boxes,which doesnt fit to the object at all. When i used the imagefeeder app before, i had same results like in Jupyter Notebook with tlt-infer. Only thing i changed on the app files, was the path to the exported .etlt model, the class label name and the folder to the test images. Any ideas on that ? Thanks in advance

What is the “detect_net_inference_imagefeeder app”?

i am following this example : . The app is included in the Issak_SDK download folder.

You mentioned “When i used the imagefeeder app before, i had same results like in Jupyter Notebook with tlt-infer.” At that time, which model were your exporting to the app?

The model i trained in the Jupyter Notebook for a own object , where i generated simulated data in Unity3d for training. When i then exported this trained model, I got same results for visualization in Jupyter Notebook and when using the detect_net_inference_imagefeeder app

I am not focusing on Isaac SDK supporting. So I need to spend time checking the issue you mentioned.
For this kind of issue, mostly it is resulted from post-processing. Is there any code or config for post-processing in this Isaac sdk app?

Okay. No there is no post processing config in the app file.

Can you share and the training spec of tlt?

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