We earlier retrained peoplenet (detecnet with resnet34) to detect age and gender. The classes of our dataset were female_adult, male_adult, and child.
When we deploy the model with the deepstream occupancy analytics application, on jetson nano with deepstream 5.1, the model doesn’t show any bounding boxes around people appearing in the frame, and also doesn’t count any of them.
The app runs without errors, but we see no results. Could you help us try to figure out how to go about solving this issue?
The pipeline seems to be wrong. The peoplenet is an object-detection model. It cannot classify age and gender.
So, for your case, it is needed to run pgie(peoplenet) and then sgie(classification model) .
We changed the pipeline to include Peoplenet as PGIE and the retrained version of it to detect age and gender as SGIE.
What we noticed is that we get a bounding box around the person but the label is always person with no indication of the age and gender labels which are (female_adult, and male_adult).
The picture below is and example of the inference we get:
We basically took images of men and women and children and labeled them: female_adult, male_adult, and child instead of just Person.
Can you please specify what you mean by using a classification network in the TAO context?
use TAO detectent_v2 network to train an object detection model. Or you can directly use peoplenet model. This model can detect person. This model is pgie.
Then, use TAO classification network to train a classification model. See more in Image Classification (TF1) - NVIDIA Docs. For the training dataset, in your case, there should be three folders. The 1st folder contains the cropped images of female_adult. The 2nd folder contains the cropped images of male_adult. The 3rd folder contains the cropped images of child. After training done, this model can set to sgie.
Could you please specify what you meant by cropped images. We will be retraining Resnet34 using the classification network, and the data we have is images: 1224x 370, that were labeled as kitty format. Do we need to change any of that?
Please find below an example of data from the dataset:
Hi,
The cropped images are used to train a classification network. When user train a classification network, the kitti format labels are not needed. I suggest you to download the classification jupyter notebook(TAO Toolkit Quick Start Guide - NVIDIA Docs) and have a try to get familiar the process.
In the 1st folder contains the cropped images of female_adult, I mean that it is better for each image to contain female_adult instead of female_adult+male_adult. I assume some training images of your original dataset contain all kinds of classes(female_adult, male_adult and child), so that is the reason I suggest you to crop it.
I am following your advice here to run the classification network. The issue I found is that when I run the classification network from our VM on GCP, in the first cell where it creates the local directories such as the folder tao-experiments, data, etc…
I can’t seem to find them on the home directory as expected.
We used the VLM before to run detectnet and this step worked fine and still does even now, so we renamed the old tao-experiments folder, and still, the classification network doesn’t create the folder correctly.
I tried creating a new environment and downloaded the dependencies, and the samples again to launch the classification network from there, but I still get the same issue.
No actually, it’s the first cell of the jupyter notebook, where we set up the local directories on the VM where we will later download the data and the different versions of the trained model. The directory looks like this:
tao-experiments—data
—classification
In our case, we get a message saying that the directory has been created successfully but we can’t find any on /home. I tried creating everything manually but at the cell where we load the data to the notebook for the training, we get an error message saying that there is no directory called tao-experiments which is confusing.
There is no update from you for a period, assuming this is not an issue anymore.
Hence we are closing this topic. If need further support, please open a new one.
Thanks