Gluoncv

Image Classification
Highlight: [Much more accurate pre-trained ResNet models on ImageNet classification](https://gluon-cv.mxnet.io/model_zoo/index.html#image-classification)

These high accuracy models are updated to [Gluon Model Zoo](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html).

- ResNet50 v1b achieves over 77% accuracy, ResNet101 v1b at 78.8%, and ResNet152 v1b over 79%.
- Training with large batchsize, with float16 data type
- Speeding up training with ImageRecordIter interface
- [ResNeXt for ImageNet and CIFAR10 classification](resnext)
- SE-ResNet(v1b) for ImageNet

Object Detection
Highlight: Faster-RCNN model with training/testing scripts

- Faster-RCNN
- RPN (region proposal network)
- Region Proposal
- ROI Align operator

- Train SSD on COCO dataset

Semantic Segmentation
Highlight: PSPNet for Semantic Segmentation
- PSPNet
- [ResNetV1b for ImageNet classification and Semantic Segmentation](resnetv1b)
- Network `dilation` is an option

Datasets
Added the following datasets and usage tutorials
- MS COCO
- ADE20k

[New Pre-trained Models in GluonCV](https://gluon-cv.mxnet.io/model_zoo/index.html)

- cifar_resnext29_16x64d
- resnet{18|34|50|101}_v1b
- ssd_512_mobilenet1.0_voc
- faster_rcnn_resnet50_v2a_voc
- ssd_300_vgg16_atrous_coco
- ssd_512_vgg16_atrous_coco
- ssd_512_resnet50_v1_coco
- psp_resnet50_ade

Breaking changes
- Rename `DilatedResnetV0` to `ResNetV1b`

Gluon CV Toolkit v0.2 Release Notes

**Note: This release rely on some features of mxnet 1.3.0. You can early access these features by installing nightly build of mxnet.**

You can update mxnet with pip:

bash
pip install mxnet --upgrade --pre
or
pip install mxnet-cu90 --upgrade --pre

Gluon CV Toolkit v0.1 Release Notes

GluonCV provides implementations of state-of-the-art (SOTA) deep learning algorithms in computer vision. It is designed for helping engineers, researchers, and students to quickly prototype products, validate new ideas, and learning computer vision.

Table of Contents
- New Features
- Tutorials
- Image Classification (CIFAR + ImageNet demo + divedeep)
- Object Detection (SSD demo + train + divedeep)
- Semantic Segmentation (FCN demo + train)

- Model Zoo
- ResNet on ImageNet and CIFAR-10
- SSD on VOC
- FCN on VOC
- Dilated ResNet
- Training Scripts
- Image Classification:
Train ResNet on ImageNet and CIFAR-10, including Mix-Up training
- Object Detection:
Train SSD on PASCAL VOC
- Semantic Segmentation
Train FCN on PASCAL VOC
- Util functions
- Image Visualization:
- plot_image
- get_color_pallete for segmentation
- Bounding Box Visualization
- plot_bbox
- Training Helpers
- PolyLRScheduler