How to incorporate a very wide video input (1920x720) into deepstream inference?

Accelerated Computing Intelligent Video Analytics DeepStream SDK
1

How to infer very wide video (1920x720) through resnet10.

• GPU - 3050Ti Mobile
• DeepStream Version -6.1
• TensorRT Version - 8.4.2-1
• NVIDIA GPU Driver Version - 515.48.07
• Issue Type - question
• How to reproduce the issue ?
I am using deepstream/samples/configs/deepstream-app/source1_usb_dec_infer_resnet_int8.txt. Just have a source of wide video instead of usb camera. Inference is working but as the video is wide, inference is not accurate. Do I have to make changes in ‘config_infer_primary.txt’ or anywhere else to make inference efficient?

3

It depends on your model but not the video. Please refer to DeepStream SDK FAQ - #21 by mchi for the accuracy issues.

4

Thanks
The intention is to use the pretrained model provided in
deepstream/samples/models/Primary_Detector

I am not so much familiar with training. I wonder if there is any parameter to tune somewhere (in pretrained detector) to make it suitable for very wide image frames (1920x720) too.

5

@Morganh Can you provide some suggestions for training?

6

You can set to fp16 or fp32 mode to check if it helps.
For training, end user can use TAO to train their own dataset.

8

I tried fp16 and fp32, but didn’t find any improvement.

I found some improvements by modifying deepstream/samples/models/Primary_Detector/resnet10.prototxt.

From

layer {
name: “input_1”
type: “Input”
top: “input_1”
input_param {
shape {
dim: 1
dim: 3
dim: 368
dim: 640 } } }

To

layer {
name: “input_1”
type: “Input”
top: “input_1”
input_param {
shape {
dim: 1
dim: 3
dim: 368
dim: 960 } } }

Is it a good approach?

9

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

It may help

11

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