hi,
I’m planning to use deepstream6 in jetson nano 2G to detect several classes of objects from camera:
bicycle
motorcycle
people
I’m quite new in this, and noticed there are many pretrained models in ngc, as i looked, seems none of them meet my requirement, some existing vehicle detection models are most build for cars.
the only dataset i know is pascal VOC and ms COCO, does this mean i can download them and directly training by TAO? or does TAO support these dataset?
thanks Morganh.
the target classes are listed in post, as:
Bicycle, Motocycle, People. (late will add a new private custom door sign, but can leave it for now).
so the questions come to me:
Any existed models in NGC can santisfy? That means i can directly use in deepstream.
if I have to train on public dataset, any tutorial to follow based on TAO toolkit to train on well-know public dataset? As I know at least those database are in different labeling format that TAO does not support.
For coco dataset, see " Prepare the COCO 2014 and COCO 2017 datasets" section, To convert the JSON labels to the KITTI format, use the coco2kitti.py Python script.
since I need seperate Bicycle and Motocycle, so just to make sure, I can’t directly use the existing model for my situation, correct?
About the training, from the Preparing State-of-the-Art Models for Classification and Object Detection… , the object detection training is based on ImageNet-pretrained weight, since training ImageNet model from scratch is not avaible here, can I replace it with a pretrained model from NGC? then I can have a quick transfer training on it by dataset PASCAL VOC, do you have remcommendation what is the base model i should refer(start transfer learning from it)?