Please provide the following information when requesting support.
• Hardware (T4/V100/Xavier/Nano/etc) Both dGPU and Jetson
• Network Type (Detectnet_v2/Faster_rcnn/Yolo_v4/LPRnet/Mask_rcnn/Classification/etc) Segformer
• TLT Version (Please run “tlt info --verbose” and share “docker_tag” here)
• Training spec file(If have, please share here)
• How to reproduce the issue ? (This is for errors. Please share the command line and the detailed log here.)
I would like to use the one provided by ngc as the Segformer Pretrained model. Can you tell me the input_image configuration? For example the following information: I think you can achieve effective pretrained effects by knowing the relevant information.
input_type: "rgb" # replace with "rgb" for color images
Looking at the Segformer description in ngc, there are no separate benchmark results for Jetson. Can I say that the fan backbone-based model is not suitable for use on Jetson devices?
These are the benchmark results when I tested with NVIDIA GeForce GTX 1660 SUPER. It is an fp16 model, and the deployable tiny model was downloaded from ngc.
trtexec --loadEngine=./cityscapes_fan_tiny_hybrid_224.engine --shapes=input:4x3x224x224 --avgRuns=1000
Latency: min = 21.3061 ms, max = 27.2076 ms, mean = 22.1975 ms, median = 21.7029 ms, percentile(90%) = 23.063 ms, percentile(95%) = 25.4922 ms, percentile(99%) = 27.0663 ms
Considering that it takes an average of 22ms, I think it is not easy to use on the Jetson AGX board. Is my experiment correct?
Deployable model is an onnx file. Can you tell me specifically how it was exported from trainable? I wonder if it was created simply by adding the model input size and dynamic option.