Simplify README

README.md

@@ -1,17 +1,25 @@
-[![travis](https://travis-ci.org/chainer/chainercv.svg?branch=master)](https://travis-ci.org/chainer/chainercv)
-
-<!--[![pypi](https://img.shields.io/pypi/v/chainercv.svg)](https://pypi.python.org/pypi/chainercv)-->
+[![](docs/images/logo.png)](http://chainercv.readthedocs.io/en/stable/)
 
+[![travis](https://travis-ci.org/chainer/chainercv.svg?branch=master)](https://travis-ci.org/chainer/chainercv)
 
-# ChainerCV
+# ChainerCV: a Library for Deep Learning in Computer Vision
 
 ChainerCV is a collection of tools to train and run neural networks for computer vision tasks using [Chainer](https://github.com/chainer/chainer).
 
 You can find the documentation [here](http://chainercv.readthedocs.io/en/stable/).
 
-![Example are outputs of detection models supported by ChainerCV](https://cloud.githubusercontent.com/assets/2062128/26337670/44a2a202-3fb5-11e7-8b88-6eb9886a9915.png)
-These are the outputs of the detection models supported by ChainerCV.
+Supported tasks:
 
++ Object Detection ([tutorial](http://chainercv.readthedocs.io/en/latest/tutorial/detection.html), [Faster R-CNN](examples/faster_rcnn), [SSD](examples/ssd))
++ Semantic Segmentation ([SegNet](examples/segnet),)
++ Image Classification ([VGG](examples/vgg),)
+
+# Guiding Principles
+ChainerCV is developed under the following three guiding principles.
+
++ **Ease of Use** -- Implementations of computer vision networks with a cohesive and simple interface.
++ **Reproducibility** -- Training scripts that are perfect for being used as reference implementations.
++ **Compositionality** -- Tools such as data loaders and evaluation scripts that have common API.
 
 # Installation
 
@@ -33,113 +41,35 @@ For additional features
 + Matplotlib
 + OpenCV
 
-
 Environments under Python 2.7.12 and 3.6.0 are tested.
 
 + The master branch will work on both the stable version (v2) and the development version (v3).
 + For users using Chainer v1, please use version `0.4.11`, which can be installed by `pip install chainercv==0.4.11`. This branch is unmaintained.
 
+# Data conventions
 
-# Features
-
-## Data conventions
-
-ChainerCV follows the following conventions.
-
-+ Channels of a color image are ordered as RGB. The shape of an image-array is CHW (channel, height, width).
-+ Axis-dependent values follow row-column order.
-  + Shape of an image is always represented as `(height, width)`.
-  + Coordinates of bounding boxes are ordered as `(y_min, x_min, y_max, x_max)`. `(y_min, x_min)` and `(y_max, x_max)` are coordinates of the top left and the bottom right vertices of a bounding box respectively.
-  + Coordinates of keypoints are ordered as `(y, x)`.
-
-## Models
-Currently, ChainerCV supports networks for object detection and semantic segmentation.
-We are planning to support networks with different tasks in the future.
-Image detection is the task of finding objects in an image and classifying the objects.
-Semantic segmentation is the task of segmenting an image into pieces and assigning object labels to them.
-Our implementations include:
-
-Detection Models
-
-+ **Faster R-CNN**
-+ **Single Shot Multibox Detector (SSD)**
-
-Semantic Segmentation 
-+ **SegNet**
-
-Models for certain tasks are designed to have common interfaces.
-For example, detection models support method that takes images and outputs coordinates, class labels and confidence scores of bounding boxes predicted around estimated regions of objects.
-The common interface allows users to swap different models easily inside their code.
-On top of that, the utilitiy codes build on top of this interface.
-For example, there is an extension of `chainer.training.Trainer` called `chainercv.extensions.DetectionVisReport` that visualizes outputs during training for models that previously stated common interface.
-
-ChainerCV internally downloads and stores files in a file system using Chainer's downloading mechanism to conveniently download pretrained models from the internet automatically.
-The convenient interface coupled with this functionality allows users to execute algorithms in two lines of code:
-
-```python
-from chainercv.links import FasterRCNNVGG16, SSD300
-# You can use Faster R-CNN instead of SSD.
-# model = FasterRCNNVGG16(pretrained_model='voc07')
-model = SSD300(pretrained_model='voc0712')
-
-
-# `bboxes` is a list of numpy arrays containing coordinates of boundnig boxes
-# around objects. `labels` and `scores` are class ids and confidence scores for
-# the boxes.
-bboxes, labels, scores = model.predict(imgs)  # imgs is a list of image
-```
-
-With the following commands, you can run a visualization demo.
-
-```
-$ cd examples/ssd  # or cd examples/faster_rcnn
-$ wget https://cloud.githubusercontent.com/assets/2062128/26187667/9cb236da-3bd5-11e7-8bcf-7dbd4302e2dc.jpg -O sample.jpg
-$ python demo.py sample.jpg
-```
-
-With the following commands, the visualizations on the top of this page can be produced.
-
-```
-$ cd examples/detection
-$ python visualize_models.py
-```
++ Image
+  + The order of color channel is RGB.
+  + Shape is CHW (i.e. `(channel, height, width)`).
+  + The range of values is `[0, 255]`.
+  + Size is represented by row-column order (i.e. `(height, width)`).
++ Bounding Boxes
+  + Shape is `(R, 4)`.
+  + Coordinates are ordered as `(y_min, x_min, y_max, x_max)`. The order is the opposite of OpenCV.
++ Semantic Segmentation Image
+  + Shape is `(height, weight)`. 
+  + The value is class id, which is in range `[0, n_class - 1]`.
 
+# Sample Visualization
 
-## Transforms
-
-ChainerCV provides functions commonly used to prepare data before feeding to a neural network.
-We expect users to use these functions together with an object that supports the dataset interface (e.g. `chainer.dataset.DatasetMixin`).
-Users can create a custom preprocessing pipeline by defining a function that describes a
-procedure to transform the incoming data.
-By decoupling preprocessing steps from dataset objects, dataset objects can be reused in a variety of preprocessing pipelines.
-
-Here is an example where a user rescales and applies a random rotation to an image as a preprocessing step.
-
-```python
-from chainer.datasets import get_mnist
-from chainer.datasets import TransformDataset
-from chainercv.transforms import random_rotate
-
-dataset, _ = get_mnist(ndim=3)
-
-def transform(in_data):
-    # in_data is values returned by __getitem__ method of MNIST dataset.
-    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 provided by ChainerCV. Like other transforms, this is just a
-function that takes an array as input.
+![Example are outputs of detection models supported by ChainerCV](https://cloud.githubusercontent.com/assets/2062128/26337670/44a2a202-3fb5-11e7-8b88-6eb9886a9915.png)
+These are the outputs of the detection models supported by ChainerCV.
 
 
 # Citation
 
-If ChainerCV helps your research, please cite the paper for ACM Multimedia Open Source competition.
-Here is a BibTex entry:
+If ChainerCV helps your research, please cite the paper for ACM Multimedia Open Source Software Competition.
+Here is a BibTeX entry:
 
 ```
 @inproceedings{ChainerCV2017,