Facial_KeyPoint training using TLT

Hi,
I am curious to know if “Facial landmark training” is possible using TLT?

Thank you

Yes, see Facial Landmarks Estimation — TAO Toolkit 3.21.11 documentation

Thank you!
I would like to know, how can I download the jupyter-notebook and other requirements?

See TAO Toolkit Quick Start Guide — TAO Toolkit 3.21.11 documentation

I mean which Model architecture name should I use, is it FACIAL LANDMARK which is FPENET or GAZENET?

Refer to Facial Landmarks Estimation — TAO Toolkit 3.21.11 documentation

fpenet.

Which tool is recommended for facial keypoint annotations?

Label Studio is giving out in the below format
[
{
“img”: “/data/upload/2/8140812d-IMG-20210930-WA0107_Ref.jpg”,
“id”: 11,
“kp-1”: [
{
“x”: 45.333333333333336,
“y”: 47.4,
“width”: 0.26666666666666666,
“keypointlabels”: [
“Nose”
],
“original_width”: 864,
“original_height”: 1152
},
{
“x”: 48.8,
“y”: 46.5,
“width”: 0.26666666666666666,
“keypointlabels”: [
“Nose”
],
“original_width”: 864,
“original_height”: 1152
},
{
“x”: 42.53333333333333,
“y”: 46.8,
“width”: 0.26666666666666666,
“keypointlabels”: [
“Nose”
],
“original_width”: 864,
“original_height”: 1152
},
{
“x”: 51.2,
“y”: 44.6,
“width”: 0.26666666666666666,
“keypointlabels”: [
“Nose”
],
“original_width”: 864,
“original_height”: 1152
},
{
“x”: 49.86666666666667,
“y”: 42.4,
“width”: 0.26666666666666666,
“keypointlabels”: [
“Nose”
],
“original_width”: 864,
“original_height”: 1152
},
{
“x”: 48.8,
“y”: 40.6,
“width”: 0.26666666666666666,
“keypointlabels”: [
“Nose”
],
“original_width”: 864,
“original_height”: 1152
},
{
“x”: 41.86666666666667,
“y”: 44.6,
“width”: 0.26666666666666666,
“keypointlabels”: [
“Nose”
],
“original_width”: 864,
“original_height”: 1152
},
{
“x”: 43.86666666666667,
“y”: 42.6,
“width”: 0.26666666666666666,
“keypointlabels”: [
“Nose”
],
“original_width”: 864,
“original_height”: 1152
},
{
“x”: 44.53333333333333,
“y”: 39.8,
“width”: 0.26666666666666666,
“keypointlabels”: [
“Nose”
],
“original_width”: 864,
“original_height”: 1152
}
],
“annotator”: 1,
“annotation_id”: 3,
“created_at”: “2021-12-14T09:59:25.569325Z”,
“updated_at”: “2021-12-14T09:59:34.222382Z”,
“lead_time”: 179.249
}
]

If Label Studio was used for annotating the facial points then how to convert it from json file of label studio into tlt’s FPENET’s format

Please refer to Facial Landmarks Estimation | NVIDIA NGC

The Sloth and Label-Studio tools have been utilized for labeling.

Please download jupyter notebook for reference.
https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html#download-jupyter-notebook

or see GPU-optimized AI, Machine Learning, & HPC Software | NVIDIA NGC directly.

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