Wenjun Wang
Wenjun Wang
> It should be 2mm/4=0.5mm. Because you have the same content on 4x **more** pixels. It becomes more fine-grained. By the way, if I take a picture through the camera,...
I means semantic segmentation
Thank you very much!
> It doesn't matter, the conclusion still holds. When I'm ready to make semantic segmentation labels, can I first process the captured photos with a super-resolution algorithm, and then do...
modified_twins_upernet ============================== Input shape: (3, 512, 512) Flops: 237.35 GFLOPs Params: 71.05 M ============================== 14.5fps convnext_deeplabv3plus ============================== Input shape: (3, 512, 512) Flops: 58.12 GFLOPs Params: 64.71 M ============================== 15.36fps...
> @wwjwy can you share your config file that swaps the backbone to ConvNext? model = dict( type='EncoderDecoder', pretrained=None, backbone=dict( type='mmcls.ConvNeXt', arch='small', out_indices=[0, 1, 2, 3], drop_path_rate=0.3, layer_scale_init_value=1.0, gap_before_final_norm=False, init_cfg=dict(...
> > In the paper, you said "The learning rate is set initially to 10−4 for both generator and critic. After the first 150 epochs we linearly decay the rate...
> You can find codes for synthetic blurring [here](https://github.com/prash030/image_processing_projects/tree/master/image_blurring_and_augmentation) Thank you very much
Thanks so much for the response!
Thank you very much for your help, I will have a try!