pretrained models? about torchsat HOT 4 OPEN

The latest master branch is support ImageNet's pre-trained model with multi-channel(>3). @geoexploring

Assuming the number of input channels/bands is 7, the first three channels is filled by ImageNet pre-trained model parameters (RGB channel). The 4, 5, 6 channel is also filled by ImageNet pre-trained RGB channel parameters. And the latest channel is filled by ImageNet pre-trained R channel parameters.

Simply speaking, torchsat copies the ImageNet parameters in order.

Examples:

In [1]: from torchsat.models.classification import resnet18

In [2]: num_class = 10

In [3]: model = resnet18(num_class, in_channels=7, pretrained=True)

In [4]: model.conv1
Out[4]: Conv2d(7, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)

In [7]: model.conv1.weight[0,0,0,0]
Out[7]: tensor(-0.0104, grad_fn=<SelectBackward>)

In [8]: model.conv1.weight[0,6,0,0]
Out[8]: tensor(-0.0104, grad_fn=<SelectBackward>)

from torchsat.

The pre-trained models for segmentation and detection is not a high priority.
You can use the ImageNet's pre-trained model for feature extraction, it also very useful.

from torchsat.

The ImageNet's pre-trained model only has 3 channels, Is this suitable for multi-channel(>3)
training of remote sensing images? @sshuair
Thanks!

from torchsat.

This solution is interesting and may be a compromise to train multi-channel satellite images using ImageNet pre-trained models.

Thanks for this excellent work , @sshuair

from torchsat.