Please provide complete information as applicable to your setup.
• Hardware Platform: Jetson Xavier NX
• DeepStream Version: 5.1
• Language: Python
I’ve trained a model using TAO (former TLT) to detect if people are wearing helmet, security glasses, face mask and security boots correclty.
In my TAO tests the model behaved correctly, made prediction and corresponding labels just fine.
Now, I’m trying to add it to my Deepstream project, to work as sgie, using “Person” class as input from pgie (TrafficCamNet).
However, the model is not predicting correctly. I based my project on python_apps/test_1 (to add tracker and sgie), test_3 (to read from file or rstp) and multistream.py (to save img to file), so my final pipeline is:
streamux → pgie (People) → tracker → sgie (Custom TAO model) → nvidconv and filter (to save img to file) → tiler → nvosd → transform → sink
The custom model was trained on 2.200 images from 4 different classes (missing_helmet, missing_glasses, missing_mask, missing_boots) all of size 272x480.
I’ve tried running my app with MUXER_OUTPUT_WIDTH/HEIGHT and TILED_OUTPUT_WIDTH/HEIGHT varying from 420x272 (same as training size) to 649x480 without success.
These are examples of images saved by my app. I’m labeling people in blue BB and the sgie detection in red BB but, as you can see in many cases the red BB is very tiny and doesn’t match with the detection:
I think I’m doing something wrong with the size of the input video because, like I said, the tests using tlt-evaluate and images taken from the same video show good results.
Can anyone help me or guide me on the correct configuration of my model.
FYI, this is mi sgie config file:sgie_config_epp.txt (3.6 KB)