Tutorial Materials for ICCV19
|size (curr.)||284944 kB|
|license||Apache License 2.0|
ICCV 2019 Tutorial: Everything You Need to Know to Reproduce SOTA Deep Learning Models
Presenter: Zhi Zhang, Sam Skalicky, Muhyun Kim, Jiyang Kang
Deep Learning has become the de facto standard algorithm in computer vision. There are a surge amount of approaches being proposed every year for different tasks. Reproducing the complete system in every single detail can be problematic and time-consuming, especially for the beginners. Existing open-source implementations are typically not well-maintained and the code can be easily broken by the rapid updates of the deep learning frameworks. In this tutorial, we will walk through the technical details of the state-of-the-art (SOTA) algorithms in major computer vision tasks, and we also provide the code implementations and hands-on tutorials to reproduce the large-scale training in this tutorial.
|8:00-8:15||Welcome and AWS Setup(Free instance available)||link|
|8:15-8:40||Introduction to MXNet and GluonCV||link,link|
|8:40-9:00||Deep Learning and Gluon Basics (NDArray, AutoGrad, Libraries)||link,link|
|9:00-9:30||Bag of Tricks for Image Classification (ResNet, MobileNet, Inception)||link||link|
|9:30-10:00||Bag of Freebies for Object Detectors (SSD, Faster RCNN, YOLOV3)||link||link|
|10:00-10:30||Semantic segmentation algorithms (FCN, PSPNet, DeepLabV3, VPLR)||link||link|
|10:30-11:00||Pose Estimation(SimplePose, AlphaPose)||link||link|
|11:00-11:30||Action Recognition(TSN, I3D)||link|
|11:30-12:00||Painless Deployment (C++, TVM)||link||link,link|
|12:00-12:15||Q&A and Closing|
Q1: How do I setup the environments for this tutorial?
A1: There will be all-in-one AWS SageMaker notebooks available for all local attendees, you need to bring your laptop and have a working email to access the notebooks.
Q2: How do I setup the environment in SageMaker after this tutorial?
A2: You can use lifetime-config to create sagemaker notebook instance using this lifetime-config. Make sure you have more than 30G disk space for the new notebook instance.
Hang Zhang, Tong He, Zhi Zhang, Zhongyue Zhang, Haibin Lin, Aston Zhang, Mu Li