2018/06/18 Deep Learning JP: http://deeplearning.jp/hacks/

1. LT: Introduction to ChainerCV
Shingo Kitagawa

2. What is ChainerCV?
• Deep learning computer vision library with Chainer.
• ChainerCV is developed by Preferred Networks.
• Feel free to make PRs and issues
• We are always welcome review and merge.
• Implementations
• Feature extraction
• Object detection
• Semantic segmentation
• Instance segmentation (New)
• and more..
• Github: https://github.com/chainer/chainercv
• Arxiv: https://arxiv.org/abs/1708.08169

3. Feature extraction
• ResNet
• Arch: He(original) and Facebook implementation is supported.
• ResNet50
• ResNet101
• ResNet152
• VGG
• VGG16
• https://chainercv.readthedocs.io/en/stable/reference/links/resnet.html
• https://chainercv.readthedocs.io/en/stable/reference/links/vgg.html

4. Object detection
• Faster-RCNN
• Faster-RCNN + VGG16
• Faster-RCNN + ResNet101
• (working)
• SSD
• SSD300 + VGG16
• SSD512 + VGG16
• YOLO
• YOLO v2 + Darknet19
• YOLO v3 + Darknet53
• Training is now working

5. Semantic segmentation
• SegNet
• PSPNet
• Training is now working
Instance segmentation
• FCIS
• FCIS + ResNet101
• Training is now working
• Mask-RCNN
• It will be implemented soon.

6. What is different from other CV library?
• Reproduce the score of original papers.
• Standardize API.
• Implement various useful functions.
• Prepared sample example code.
• Read a lot of WEB documentation.
• Easy to install and use.
• A lot of test codes for maintenance.

7. Reproduction of the original score
• Stable: Faster-RCNN, SSD, YOLO, SegNet
• No less than 0.5 point lower or even higher
• Use it for the comparison with your method!
• Experimental: FCIS, PSPNet (New)
• Implemented but score is around 1.0 point lower 
• But we are still working to reproduce original paper.

8. API standardization
• Models have the same utility functions.
• prepare() : Preprocess input
• predict() : Predict and return output
• use_preset() : Set parameters
• Naming conventions
• It is strictly fixed in the source code.

9. Useful functions
• Transforms
• crop, flip, resize, translate, pca_lighting, scale, sized_crop
• Random + crop, expand, flip, rotate, sized_crop
• bounding box, image, point
• Visualizations (matplotlib)
• vis_bbox, vis_image, vis_point
• vis_instance_segmentation, vis_semantic_segmentation
• Utils
• bbox_iou, mask_iou, nms, I/O
• Evaluations
• COCO, VOC and more.

10. Prepared example codes
• chainercv/examples
• Demo:
• python demo.py
• Training:
• python train.py
• Evaluation:
• python eval_voc.py

11. WEB documentation (sphinx)
• DOC: https://chainercv.readthedocs.io/en/stable/

12. Easy to install
• pip install chainercv
• Additional requirements
• ChainerMN, Matplotlib, OpenCV, SciPy
• Datasets and models will be installed automatically.
• Default: ~/.chainer/pfnet/chainercv
• Of course, you can install it manually by yourself.
• ROS integration
• Rosdistro: python-chainer-pip, python-chainercv-pip

13. Test codes
• Github + Travis CI
• chainercv/tests

14. Other Chainer family and useful pages
• Chainer is super awesome!
• Chainer family
• ChainerMN, ChainerRL, ChainerUI
• ONNX: ONNX-Chainer
• Chainer Research
• https://github.com/pfnet-research
• chainer-chemistry, sngan, picking-instruction
• Chainer community
• https://github.com/chainer-community/awesome-chainer
• https://github.com/chainer-community/chainer-info
• Forum / Slack / Twitter

15. If you still cannot love chainer ,
• PyTorch: kuangliu/torchcv
• Inspired from chainercv 
• Easy to use, read and write 
• https://github.com/kuangliu/torchcv
• Tensorflow: tensorflow/models
• A lot of model 
• Good to read and study, but not good to use.
• Caffe2: facebookresearch/Detectron
• Easy to use, read and write 
• Caffe2 is hard to install 
• Mxnet: apache/incubator-mxnet
• A lot of model, but no proof of scores.
• Spaghetti codes! 