GitLab

* Initial version of the segmentation examples

WIP

* Cleanups

* [WIP]

* Tag where runs are being executed

* Minor additions

* Update model with new resnet API

* [WIP] Using torchvision datasets

* Improving datasets

Leverage more and more torchvision datasets

* Reorganizing datasets

* PEP8

* No more SegmentationModel

Also remove outplanes from ResNet, and add a function for querying intermediate outputs. I won't keep it in the end, because it's very hacky and don't work with tracing

* Minor cleanups

* Moving transforms to its own file

* Move models to torchvision

* Bugfixes

* Multiply LR by 10 for classifier

* Remove classifier x 10

* Add tests for segmentation models

* Update with latest utils from classification

* Lint and missing import

* Add ShuffleNet v2

Added 4 configurations: x0.5, x1, x1.5, x2
Add 2 pretrained models: x0.5, x1

* fix lint

* Change globalpool to torch.mean() call

* Add MobileNet V2

* Remove redundant functions and make tests pass

* Simplify a bit the implementation

* Reuse ConvBNReLU more often

* Remove input_size and minor changes

* Py2 fix

* Add GoogLeNet (Inception v1)

* Fix missing padding

* Add missing ReLu to aux classifier

* Add Batch normalized version of GoogLeNet

* Use ceil_mode instead of padding and initialize weights using "xavier"

* Match BVLC GoogLeNet zero initialization of classifier

* Small cleanup

* use adaptive avg pool

* adjust network to match TensorFlow

* Update url of pre-trained model and add classification results on ImageNet

* Bugfix that improves performance by 1 point

* added Inception v3 to index;
* document pretrained models;
* fix typo.

* Add documentation for transforms

* document and remove unused imports in mnist.py

* document lsun, mscoco datasets

* rest of the datasets documented

* Clean up the documentation in other functions

* Add links for datasets

* Add more documentation

* pep8 fix

Fixes #152

* Add SqueezeNet 1.0 and 1.1 models
* Selectively avoid inplace in SqueezeNet
* Use Glorot uniform initialization in SqueezeNet
* Make all ReLU in SqueezeNet in-place
* Add pretrained SqueezeNet 1.0 and 1.1
* Minor fixes in SqueezeNet models

Also add pre-trained ResNet-152 model.

  ResNet-152: Prec@1 78.312 Prec@5 94.046