PyPI 官网下载 | chainercv-0.4.5.tar.gz

<!--[](https://travis-ci.org/pfnet/chainercv)--> <!--[](https://pypi.python.org/pypi/chainercv)--> # ChainerCV ChainerCV is a collection of tools to train neural networks for computer vision tasks using [Chainer](https://github.com/pfnet/chainer). You can find the documentation [here](http://chainercv.readthedocs.io/en/latest/). This project is under heavy development, and API has been changing rapidly. # Installation ``` pip install chainercv ``` ### Requirements + [Chainer](https://github.com/pfnet/chainer) and its dependencies + Pillow For additional features + Matplotlib + OpenCV + Scikit-Learn Environments under Python 2.7.12 and 3.6.0 are tested. # Features ## Transforms ChainerCV supports functions commonly used to prepare image data before feeding to neural networks. We expect users to use these functions together with a dataset object (e.g. `chainer.dataset.DatasetMixin`). Many of the datasets prepared in ChainerCV are very thin wrappers around raw datasets in the filesystem, and the transforms work best with such thin dataset classes. The users can create a custom preprocessing pipeline by defining a function that describes procedures to transform data. Here is an example where the user rescales input image and data augments it by randomly rotation. ```python from chainer.datasets import get_mnist from chainercv.datasets import TransformDataset from chainercv.transforms import random_rotate dataset, _ = get_mnist(ndim=3) def transform(in_data): # in_data is the returned values of VOCSemanticSegmentationDataset.get_example img, label = in_data img -= 0.5 # rescale to [-0.5, 0.5] img = random_rotate(img) return img, label dataset = TransformDataset(dataset, transform) img, label = dataset[0] ``` As found in the example, `random_rotate` is one of the transforms ChainerCV supports. Like other transforms, this is just a function that takes an array as input. Also, `TransformDataset` is a new dataset class added in ChainerCV that overrides the underlying dataset's `__getitem__` by calling `transform` as post processing. # Automatic Download ChainerCV supports automatic download of datasets. It uses Chainer's default download scheme for automatic download. All data downloaded by ChainerCV is saved under a directory `$CHAINER_DATASET_ROOT/pfnet/chainercv`. The default value of `$CHAINER_DATASET_ROOT` is `~/.chainer/dataset/`.