with your guidance, I have successfully trained the RESNET with the plant dataset and also my own dataset.
Now I am working on testing the object detection with the Open_images dataset as given in this tutorial and then later with my own dataset again
Hi @karishmathumu, when you cloned the repo with git, did you use the --recursive option? I suspect that you are missing the detection/ssd submodule, and those files aren’t on your disk yet.
If you didn’t use --recursive, try doing this:
cd /path/to/your/jetson-inference
git submodule update --init
Thank your response. I am sorry, I missed the --recursive option while cloning.
And as you said, I went into the jetson-inference path and ran “git submodule update --init” and
then went into the detection/ssd path and ran
Sorry Sir, I am not sure how to re-clone the existing jetson-inference directory in my workspace.
So, I tried to clone it from the start with the --recursive flag in a new workspace folder.
But when i do the make step while compiling the project, i am getting an error. tk1_ws/jetson-inference/build$ make
This error is also repeating when i just doing the make in the old path- tk_ws/src/jetson-inference/build $ make
Hi @karishmathumu, this is a different error, it appears that it can’t find the CUDA Toolkit on your system. Can you do the following?
ls -ll /usr/local/cuda
/usr/local/cuda/bin/nvcc --version
If you don’t have CUDA Toolkit installed, you can install it with sudo apt-get install cuda-toolkit-*
If you have gotten the system into an unknown state with the symbolic links, I would just re-flash the device. I think the YOLO issues are probably related to the same thing.
OK gotcha - glad you were able to get the thing with your CUDA Toolkit figure out.
If you are still having problems compiling jetson-inference, I would recommend re-cloning the repo with --recursive (although I don’t see what the error was from your screenshot, so not exactly sure). You probably want to save your data from jetson-inference/python/training/detection/ssd/data/fruits so that you don’t have to download it again.
OK, it seems like your CUDA Toolkit is good now (those were warnings, not errors)
Regarding OpenCV, it’s unclear if you have it installed or not. Can you try sudo apt-get install nvidia-opencv-dev ? And if that doesn’t work, try sudo apt-get install libopencv-dev libopencv-python python3-opencv
Hi @karishmathumu, I’m not familiar with building YOLO or darknet, so you may want to open a new topic about that if you are still having trouble. Thanks.
I have created my own dataset with the 3 different labels using the Yolo_Label tool.
It has created a text file for each image with bouding box information.
i have created a folder named “samples”, data/samples and within it
the folder having the images and txt files of all the images of the 3 classes.
labels.txt file
Then I tried the followign command, python3 train_ssd.py --dataset-type=voc --data=data/samples --model-dir=models/metal_can_pet_blue_transparent_pet_sleeved_images
which had an error. How do I rectify this Sir.
Do i have to segregate it into train, validation and test folders of my images?
Is the Yolo_Label tool able to export your dataset in Pascal VOC format? The structure of Pascal VOC is what train_ssd.py expects (hence I use tools like CVAT.org that are able to export in that format)
To understand the folder structure of Pascal VOC dataset and it’s XML annotation formats, I recommend downloading a copy of the original Pascal VOC dataset from here.
Hi @karishmathumu, the error from this command says that it can’t connect to your MIPI CSI camera. Are you using a MIPI CSI camera? If not, you can run it on some test images from your dataset. See here for the different ways you can specify which data to run the detectnet program on: