Tech kitchen: Object detection and Classification
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Transcript of Tech kitchen: Object detection and Classification
Warning!This presentation contains images that may cause severe drooling and stomach grumbling.
�������@cookpad
ImageNet Large Scale Visual Recognition Competition
http://www.image-net.org/challenges/LSVRC/
ILSVRC 2010 taskClassificationFor each image, algorithms will produce a list of at most 5 object categories in the descending order of confidence.
http://www.image-net.org/challenges/LSVRC/
http://cs231n.stanford.edu/syllabus.html
Classification + Localization
ILSVRC 2012 tasks1. Classification
2. Classification with localization
3. Fine-grained classification
Fine-grained classification
http://www.image-net.org/challenges/LSVRC/
AlexNet
Imagenet classification with deep convolutional neural networks
A Krizhevsky, I Sutskever, GE Hinton, Advances in neural information processing systems, 1097-1105
Object Detection
http://cs231n.stanford.edu/syllabus.html
Deep Learning
https://devblogs.nvidia.com
ILSVRC 2015 tasks
1. Object detection
2. Object localization
3. *Object detection from video
4. *Scene classification
ILSVRC 2016 tasks1. Object localization
2. Object detection
3. Object detection from video
4. Scene classification
5. Scene parsing
ゴールFind food in the image, draw a bounding box around the food item, including the dish, if visible.
ground truth
bounding box
> 0.9
We count it as a positive detection if Intersection over Union ratio is
greater than 0.9.
number of true positives number of ground truth boxes
number of true positives number of generated boxes
再現率 (precision)
��� (recall)
1. Build a classifier
2. Pick Regions of Interest
3. Run classifier on each region
4. Remove duplicate detections
IDEA
Fast, Faster R-CNN
(2013) Rich feature hierarchies for accurate object detection and semantic segmentationRoss Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
(2016) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksShaoqing Ren, Kaiming He, Ross Girshick, Jian Sun
(2015) Fast R-CNNRoss Girshick
問題
1. Computational cost
2. Context is important
3. ...but context can be
confusing.
hand
food
grass
food
http://pixabay.com
Single Shot Detector
(2015) SSD: Single Shot MultiBox DetectorWei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg
Either The Least Or Most Employable Person Ever
- The Huffington Post
github.com/pjreddiepjreddie.com/darknet/www.kaggle.com/16295-pjreddie
Joseph Redmon
You Only Look Once
(2015) You Only Look Once: Unified, Real-Time Object DetectionJoseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
(Dec. 2016) YOLO9000: Better, Faster, StrongerJoseph Redmon, Ali Farhadi
You Only Look Once: Unified, Real-Time Object DetectionJoseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
YOLO in Context