Please provide the following information when requesting support.
• Hardware TITAN Xp
• Network Type Detectnet_v2
• TLT Version
Configuration of the TAO Toolkit Instance
dockers: [‘nvidia/tao/tao-toolkit-tf’, ‘nvidia/tao/tao-toolkit-pyt’, ‘nvidia/tao/tao-toolkit-lm’]
format_version: 2.0
toolkit_version: 3.22.02
published_date: 02/28/2022
• Training spec file
train sepc file: peoplenet_spec.txt (3.2 KB)
tfrecods log file: tfrecords.log (57.6 KB)
training log file: train.log (334.4 KB)
Hi, i’m trying to use peoplenet with pretrained model resnet18_peoplenet.tlt to train on custom dataset, i map the mask and non-mask to face class, but the average percision is always 0.
Hope someone help me,very thanks
@Morganh Tankyou very much!
I have a little question about the relationship between enable_auto _resize and output _image _height and output _image _weight.
In this experiment, i manually preprocess the dataset image size to 544x960x3(HWC), the original image is 1296x1296x3, if i want to use enable_auto_resize param, the outpu_image_height and output_image_weight how to set? is 544x960 or 1296x1296
For detectnet_v2 network, firstly, it is necessary to resize images/labels to the same resolution offline. You already did this. You mention that you resize images/labels to 960x544(WxH)
So, if you want to train a 960x544 model(set output _image _weight:960 and set output _image _height: 544) , you need not set enable_auto_resize because your images/labels are already 960x544.
If you want to train a 640x480 model(set output _image _weight:640 and set output _image _height:480), you need to set enable_auto_resize:true because your images/labels are not 640x480.