Zekun ZHANG ZekunZh

Updated 19/10/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

One important lesson: Always verify input images of NN before starting a long train !

Lesson 1: always verify input training images before you run a long training !

Lesson 2: always verify train losses during training !

Lesson 3: always verify predicted bboxes during evaluation !

Lesson 4: the devil lies in the smallest details, do not overlook small but meaningful signals !

VALID_PARTS = ['ANKLE', 'ARM', 'CLAVICLE', 'ELBOW', 'FINGER', 'FOOT', 'FOREARMBONE', 'HAND', 'HIP', 'HUMERUS', 'KNEE', 'LEG', 'PATELLA', 'PELVIS', 'SHOULDER', 'WRIST']

Remove PELVIS and HIP for now, because of the limitation of images

ls | parallel -n2000 mkdir {#}\;mv {} {#}

-n2000 takes 2000 arguments at a time and {#} is the sequence number of the job.

Start from FP/FN

For FP, find a sweet point of confidence threshold that corresponds to X (X=0.3) mean FP/study, in order to predict the bboxes that are possible missings in GT.

Note : confidence threshold up -> mean FP/study down -> total FP down -> less time & effort, but can miss more good preds
For FN, find a sweet point of confidence threshold that corresponds to X (X=?) mean FN/study, in order to show the GTs that are not detected / badly detected by the model.

	So you've cloned somebody's repo from github, but now you want to fork it and contribute back. Never fear!

	Technically, when you fork "origin" should be your fork and "upstream" should be the project you forked; however, if you're willing to break this convention then it's easy.

	* Off the top of my head *

	1. Fork their repo on Github
	2. In your local, add a new remote to your fork; then fetch it, and push your changes up to it

	git remote add my-fork git@github...my-fork.git

	class ImageBaseAug(object):
	def __init__(self):
	sometimes = lambda aug: iaa.Sometimes(0.5, aug)
	self.seq = iaa.Sequential(
	[
	# Blur each image with varying strength using
	# gaussian blur (sigma between 0 and 3.0),
	# average/uniform blur (kernel size between 2x2 and 7x7)
	# median blur (kernel size between 3x3 and 11x11).
	iaa.OneOf([

	az vm create --resource-group bushi-RG1 \
	--name glm-bushi-2 --nics bushi-nic-2 \
	--size Standard_DS1_v2 --os-type Linux \
	--attach-os-disk glm-bushi-2 --attach-data-disks glm-bushi-disk-2 --plan-name linuxdsvmubuntu --plan-product linux-data-science-vm-ubuntu --plan-publisher microsoft-ads