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Training the model #20
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Hi dy1ngs0ul, Thanks for reaching out! You may find the training configuration files in this directory Please let me know if you have any questions. Best, |
Thanks for your help |
@jaybdub , Thanks for your excellent work! "model": {
"name": "densenet121_baseline_att",
"kwargs": {
"cmap_channels": 18,
"paf_channels": 42,
"upsample_channels": 256,
"num_upsample": 3
}
}, |
Hi guys! Have any of you succesfully completed any training using the script provided within the repo? |
I solved it myself, thank you anyway! |
Hey all, I'm having a similar error as @NicolaGugole using the training dataset downloaded through the provided shell script. |
In my case I had to change the annotation file because I noticed a difference between the annotation keypoints number (17 keypoints) and the human_pose.json number (18 keypoints). This difference in tensor sizes is weird in my opinion. I noticed that in this config file (https://github.com/NVIDIA-AI-IOT/trt_pose/blob/master/tasks/human_pose/experiments/resnet18_baseline_att_224x224_A.json) the devs used a "modified" version of the json file. I hope in the near future we'll have the opportunity to take a look at the modified version of these json files (maybe the devs could upload the files to this repo). So I have a question @OliverGuy : did you just change the kwargs cmap_channels and paf_channels in the json file referenced earlier? Did that do the job? I tried to do the same but ended up with other conflicts. Sorry for bothering you all, |
@NicolaGugole I only modified those in the json, but I'm having issues with CudNN not finding the convolution algorithm (see #54). |
You have to pre-process the coco annotations. This adds the "Neck" keypoint (midpoint of shoulders) so that you will have 18 keypoints. Use the command:
|
Did you figure out what |
Hello Nvidia AI-IOT team,
First of all thank you very much for your effort in creating this code. I am Zeyan and currently working on real time pose estimation implementation on Jetson AGX Xavier.
My goal is to use Depths image (from Intel real sense camera) and check whether the depths information could help improve the performance of pose estimation or not.
Before I conduct my experiments. First I wish to train the model to act as an base line for our experiments. From your training script it seems config.json file is required to trained the network. As i wish to follow your parameters for this baseline training. It would be great if you could provide me your conifg file so that I could follow your step and parameters to train your model.
Thanks in advance for your help and support. I will be looking forward for your reply. Please let me know if you have anything to say,
Thanks
Dr. Zeyan Oo
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