Please provide the following information when requesting support.
• Hardware (T4/V100/Xavier/Nano/etc)
Ubuntu, x86, RTX3090
• Network Type (Detectnet_v2)
• TLT Version (Please run “tlt info --verbose” and share “docker_tag” here)
• Training spec file(If have, please share here)
• How to reproduce the issue ? (This is for errors. Please share the command line and the detailed log here.)
I have a small proprietary dataset which contains 4 classes of images, the annotations statistics when doing dataset convert (in tao jupyter note book) is:
iva.detectnet_v2.dataio.dataset_converter_lib:
Wrote the following numbers of objects:
b'people': 2275
b'dog': 1194
b'cat': 1102
b'horse': 450
so this is a typical imbalanced dataset.
I noticed the properties of class_weight
and enable_autoweighting
under section cost_function_config
both in train and retrain spec are looked like to help the scenario, I want to understanding how to set them for my case.
- if set
enable_autoweighting
totrue
, then does it mean theclass_weight
are all meaningless? - does
class_weight
for different class should just simply reflect the linear relation of their annotation count?
then theclass_weight
for my case is: 10, 20, 20, 40 respectively for the 4 classes? - how much improve I can expect after apply these settings?