Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) Ubuntu 1804
• DeepStream Version 5.0
• TensorRT Version 7.2.2.1+cuda10.2
I’m running a classification model as secondary model on deepstream. I already tested my model outside the deepstream with good results. However, the results of the classification model in deepstream are substantial poorly. The deepstream is returning the wrong label with 1.0 confidence in almost all cases. Does anyone have any idea what’s going on? Additionally, Is it possible I run the classification model on osd_sink_pad_buffer_probe after I get the detection result?
Thanks a lot!
My configuration for the classification model is:
[property]
gpu-id=0
net-scale-factor=1
network-type=1
uff-file=models/resnet18-32is32b.uff
model-engine-file=models/resnet18-32is32b.uff_b1_gpu0_fp32.engine
labelfile-path=models/resnet_labels.txt
uff-input-blob-name=data
infer-dims=3;32;32
output-blob-names=dense/Softmax
force-implicit-batch-dim=1
batch-size=1
network-mode=0
input-object-min-width=32
input-object-min-height=32
process-mode=2
model-color-format=0
gie-unique-id=2
operate-on-gie-id=1
operate-on-class-ids=3
is-classifier=1
classifier-async-mode=0
classifier-threshold=0.8