Possibility of QAT training for Jetson devices for yolov4_tiny model with pruned etlt model

Accelerated Computing Intelligent Video Analytics TAO Toolkit
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• Hardware GeForce RTX 4070 Ti
• Network Type (Detectnet_v2)
• TLT Version (format_version: 2.0, toolkit_version: 4.0.1)

This is link to my previous question for more reference

Very low precision while Training detectnet_v2 model using custom data in TAO

Now I have got considerable accuracy of about 0.79 mAP and my aim is to deploy this model on jetson xavier NX device in deepstream.

I have generated following files in export folder

  • cal.bin -------- calibration cache file
  • cal.tensorfile ---------tensorfile
  • trt.engine ------- fp32 engine file
  • trt.engine.fp16 ----------fp16 engine file
  • trt.engine.fp8 ----------fp8 engine file
  • labels.txt ----------------- name of classes
  • nvinfer_config.txt ----------------- it generates incomplete deepstream related config file( not sure though in detectnet_v2 it is mentioned.)

Now i want to deploy it on jetson for that QAT training is required but i assumed for training it will be using pretrained .etlt but i just observed that it is using downloaded .hd5 model

pretrain_model_path: “/workspace/tao-experiments/yolo_v4_tiny/pretrained_cspdarknet_tiny/pretrained_object_detection_vcspdarknet_tiny/cspdarknet_tiny.hdf5”

  • so is there a way i can use .etlt model instead for training model with pretrained model and train from scratch?
  • Also i want to ask if i use int8 model trained with TAO (and not QAT trained model) directly on jetson then is it compulsory to use TAO converter?? also will it give same accuracy after conversion?
  • finally for deployment on jetson should i train QAT trained int8 model or convert the model on jetson as i stated earlier?
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You can use the tlt model in the existing result folder. Set it as pretrained model and trigger new training.

User can deploy non-QAT model or QAT model.
TAO-converter is an option which is mentioned in previous TAO version. It can be still used.
Currently, tao-deploy can also generate the tensorrt engine as well.

To deploy in deepstream, please use GitHub - NVIDIA-AI-IOT/deepstream_tao_apps: Sample apps to demonstrate how to deploy models trained with TAO on DeepStream
You can deploy the .etlt model and key directly. Then deepstream will help generate the engine.
https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps/blob/master/configs/yolov4-tiny_tao/pgie_yolov4_tiny_tao_config.txt#L31-L32

You can also comment out above two lines if you already use tao-converter or tao-deploy to generate engine. Then, just set https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps/blob/master/configs/yolov4-tiny_tao/pgie_yolov4_tiny_tao_config.txt#L29

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