Trying to implement transfer learning using peoplenet model

Docker_tag:–> v3.21.08-py3
Network Type → detectnet_v2
spec file → spec.txt (4.2 KB)

log file →
log file.txt (26.3 KB)


I am trying to add people images along with the phone class and trying to implement transfer learning using peoplenet (resnet18_peoplenet.tlt) model file.

after freezing initial 3 blocks I getting low map for person but the phone map is good.

Phone map → 86
person map → 14

I am not able to understand why person map is not getting increase.I want to increase person class map

Hi @user86169
As we synced previously, did you ever try to run with KITTI dataset?
May I know for your current result, which is the dataset and how many training dataset is it?

does kitti dataset have people class also?

Yes, the public KITTI dataset has people class.

is there any way through which i can extract some images of people along with there annotations from kitti dataset?

You can download the pubic dataset according to the guide in jupyter notebook GPU-optimized AI, Machine Learning, & HPC Software | NVIDIA NGC

Then, search the lable files which contain pedestrian class.
Then, move all of their corresponding images from images folder.

but I want person class images not pedestrian class

You can view the KITTI images to check if it meets your requirement.
In KITTI dataset, there are “pedestrian” and “person_sitting” .

So, in the detectnet_v2 spec files(TAO Toolkit Quick Start Guide — TAO Toolkit 3.21.11 documentation), it set below class_mapping.

target_class_mapping {
key: “pedestrian”
value: “pedestrian”
target_class_mapping {
key: “person_sitting”
value: “pedestrian”

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