Cfg file for running the demo of MobileNetv2 and MobileNetV3 models

[jhb86253817]

Cfg file for running the demo of MobileNetv2 and MobileNetV3 models

[jhb86253817]

Hi, Here is the cfg file of mobilenetv3 on 300W:

class Config():
    def __init__(self):
        self.det_head = 'pip'
        self.net_stride = 32
        self.batch_size = 16
        self.init_lr = 0.0001
        self.num_epochs = 120
        self.decay_steps = [60, 100]
        self.input_size = 256
        self.backbone = 'mobilenet_v3'
        self.pretrained = True
        self.criterion_cls = 'l2'
        self.criterion_reg = 'l1'
        self.cls_loss_weight = 10
        self.reg_loss_weight = 1
        self.num_lms = 68
        self.save_interval = self.num_epochs
        self.num_nb = 10
        self.use_gpu = True
        self.gpu_id = 0

For get_meanface function, all backbones share the same file, and the data name corresponds to training data, e.g., datat_300W, WFLW, etc.

[DanielXu123]

I may have confused.
Have you offered the facial landmark model trained on MobileNetv3 or MobileNet-v2?
The model mentioned above "mobilenet-v3-large model (mobilenetv3-large-1cd25616.pth) in ~/PIPNet/lib " is the pretrained model, not the final facial landmark model, is that right ?

[DanielXu123]

Will you provid the trained models of MobileNet-V3 and MobileNet-v2 ? It woild be very appreciated.

[jhb86253817]

I did not provide trained model for mobilenets previously. And the one named '"mobilenet-v3-large model (mobilenetv3-large-1cd25616.pth)" is an ImageNet pretrained model for initialization.
I just updated the trained models by adding mobilenet v2 and v3 trained on 300W and WFLW. You may check the shared Google Drive in the ReadMe.

[DanielXu123]

Thx!!
I will test the model. Thanks for your sharing.
For the model you trained on WFLW, the config file should be like this:

class Config():
    def __init__(self):
        self.det_head = 'pip'
        self.net_stride = 32
        self.batch_size = 16
        self.init_lr = 0.0001
        self.num_epochs = 120
        self.decay_steps = [60, 100]
        self.input_size = 256
        self.backbone = 'mobilenet_v3'
        self.pretrained = True
        self.criterion_cls = 'l2'
        self.criterion_reg = 'l1'
        self.cls_loss_weight = 10
        self.reg_loss_weight = 1
        self.num_lms = 98
        self.save_interval = self.num_epochs
        self.num_nb = 10
        self.use_gpu = True
        self.gpu_id = 0

Is that right ?
Again, thanks for your awasome work.

[jhb86253817]

Yes, I think so. The only difference is the number of landmarks.
Your're welcome :)