pretrained-models.pytorch
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Cadene
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.
https://github.com/Cadene/pretrained-models.pytorch/blob/56aa8c921819d14fb36d7248ab71e191b37cb146/pretrainedmodels/utils.py#L54-L59 I assume we keep aspect ratio when `preserve_aspect_ratio` is True, this way. code snippt should be ```python if preserve_aspect_ratio: height = int(self.input_size[1] / self.scale) width = int(self.input_size[2] / self.scale)...
Hi, currently `vggm` is not an usable architecture even though it is already implemented. Could you please fix it? Thank you!
when I load the xception model,I meet: model_name = 'xception' # could be fbresnet152 or inceptionresnetv2 model = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet') model.eval() the error msgs: RuntimeError: Error(s) in loading state_dict for...
PNASNet-5-Large is already in the repo. Would it be possible to include PNASNet-5-Mobile? Thanks!
I don't know what to say to thank you for this project, it helps a lot of people, and I would like to suggest, if you can, collecting some loss...
i load a bninception model---https://github.com/Cadene/pretrained-models.pytorch/tree/master/pretrainedmodels/models It has been pre trained. when i load a bninception model in tsn---https://github.com/yjxiong/tsn-pytorch/ After dozens of epoch, loss has been stuck in a large number....
Is it possible to incorporate the pretrained ResNeXt-101-32x8d model from https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl The source is from https://github.com/facebookresearch/Detectron/blob/master/configs/12_2017_baselines/e2e_faster_rcnn_X-101-32x8d-FPN_2x.yaml.
Hello, do you have the pretrained model of GoogLeNet as in the 2015 paper? Thanks!
Hi guys, First thanks very much for the package! I found it very easy to use and am very grateful that you guys put in the effort to make this....
I want use PNASNet5Large as encoder for my Unet here is my wrong aproach for the PNASNet5Large but working for resnet: class UNetResNet(nn.Module): def __init__(self, encoder_depth, num_classes, num_filters=32, dropout_2d=0.2, pretrained=False,...
