Why Open Images? Why Open Images?
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- Irma Phillips
- 5 years ago
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Transcription
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2 Why Open Images? It s open! Restrict to images with CC_BY license no copyright problems Can even use it commercially Enables legally safe crowdsourcing Why Open Images?
3 Why Open Images? Start from Flickr, then dedupe against the web biased away from simple images "Ball" in Google Image Search Not scraped based on class keywords (all of CC on Flickr) natural class statistics, no initial design bias Why Open Images? "Ball" in Open Images
4 Open Images V4 dataset Open Images V4 dataset
5 Open Images V4 dataset Open Images V4 dataset
6 Open Images V4 dataset Open Images V4 dataset
7 Open Images V4 dataset (train+val+test) Open Images V4 dataset Subset with bounding boxes: 1.9 million images (1.74M train, rest val+test) 15.4 million boxes 8.1 boxes/image 600 classes Subset with image-level labels: 5.8 million images 14.9 million labels 19.8 thousand classes
8 Open Images V4 dataset: box annotation process Does this image contain a cat? We applied a classifier to each image and sent labels with sufficient score for human verification. We boxed all object instances of all positive labels 90% using Extreme Clicking Draw extreme points on the cat [Papadopoulos, Uijlings, Keller, Ferrari, ICCV 2017] 10% using Box Verification Series [Papadopoulos, Uijlings, Keller, Ferrari, CVPR 2016] Is this a correct box for a cat? image labels not exhaustively annotated but boxed all instances for the labels we have Open Images V4 dataset NO YES
9 Open Images V4 dataset: comparison to ILSVRC-det and COCO -det 3x 4x 29x 7x 15x 17x (3.0 on a 80k image subset) Complex images (many objects per image) Open Images V4 dataset COCO has segmentations though!
10 Open Images V4 dataset: comparison to ILSVRC-det and COCO Open Images Open Images V4 dataset ILSVRC-Det COCO
11 Open Images V4 dataset Object size distribution similar to COCO (many small objects) 43% of the objects occupy less than 1% of the image area Open Images V4 dataset
12 Open Images Challenge 2018 Based on Open Images V4 Two tracks: Object Detection Track Visual Relationship Detection Track 50K prize money fund 4 months to further push the capabilities of computer vision Open Images Challenge dataset a sandy beach, the ocean, a few people walking, some trees, grass, and buildings a woman walking her dog right there! Oh yeah, and there is a man holding a plastic cup.
13 Track 1: Object Detection Task: detect objects on an image object bounding box class label Christmas tree Training set: 12,195,144 bounding boxes 1,743,042 images 500 classes removed some broad (e.g. "clothing") and some infrequent classes (e.g. "paper cutter") Track 1: object detection Cat Table Food
14 Track 1: Object Detection - why challenging Challenging images Many classes (500) Imbalanced class distribution Class hierarchy Non-exhaustive annotations for classes (but all objects annotated for those we have) Open Images Challenge dataset
15 Track 2: Visual Relationships Detection Task: detect relations between objects Two objects boxes and classes Relationship between them Man box Training set: 374,768 relationship annotations 3,290,070 boxes 1,743,042 images 329 distinct relation triplets 62 different object classes Track 2: visual relationship detection Microphone box Must identify which of the two men holds the microphone!
16 Track 2: Visual Relationships Detection Challenges: Challenging images Class co-occurrence not sufficient to predict the relationship High relationship triplets imbalance Track 2: Visual relationship detection
17 Today s program Today s program
18 Challenge organizers Alina Kuznetsova Vittorio Ferrari Challenge organizers Jasper Uijlings Jordi Pont-Tuset Jake Walker Matteo Malloci Rodrigo Benenson
19 All contributors to the Open Images Dataset Challenge organizers
20 Thanks to... Common Visual Data Foundation For hosting the data through AWS Special thanks to Tsung-Yi Lin Figure Eight For hosting the data Kaggle For hosting the competition Thanks to our partners
21 Thanks to our sponsors Thank you to our sponsors
22
23 Materials after this slide
24 Open Images Challenge
25 Track 2: Visual Relationships Detection 1. Select relationships: co-occurrences analysis on image-level labels level + common sense 2. Annotations: annotators verification Man Guitar Track 2: visual relationship detection Woman Guitar
26 The next slide I usually show as a big summary of the key stats and facts
27 - 600 object classes M bounding-boxes on 1.9M images - 10x over existing datasets - Complex images (average 8 boxes) PLAYS AT - Visual Relationship Detection annotations - Challenge at ECCV 2018 Open Images V4
28 Slides used by Neil in Google Research Conference 2017 below this point
29 People Teams
30 Large scale image dataset, as of V3: High-Level Overview of Open-Images 9M images, ~20M labels, ~4M boxes Safe for commercial use (CC-BY) Foster industry + academic collaboration
31 Dataset Motivation and Origins Image-Level Groundtruth Box Groundtruth (and Beyond) Next Steps Agenda
32 Vision Tasks We Want To Enable Winter Snow Sled Sledding Headgear tvmonitor Jug Bottle Image Classification Object Detection In the future: Segmentation OCR Captioning VQA... Vision Tasks We Want To Enable houseplant
33 Image-Level Labels: Bicycle wheel Person Land vehicle Human body Clothing Footwear Arm Bike Bicycle frame Extreme sport Cycle sport Sports equipment Freestyle bmx Sports Vehicle Wheel Bicycle motocross Box Groundtruth Visualizers: validation set, train set
34 Image-Level Labels: Sofa bed Loveseat Chair Lobby Couch Interior design Living room Suite Table Property Room Arab cuisine Furniture Box Groundtruth Visualizers: validation set, train set
35 Image-Level Labels: Knife Weapon Vegetable Quiche Cuisine Dessert Dish Tart Baked goods Baking Sweet potato pie Breakfast Pie Box Groundtruth Visualizers: validation set, train set
36 Dataset Motivation and Origins Problem: Other datasets are copy-encumbered Solution: restrict to images with CC_BY license Enables Crowdsourcing Google Village Crowdsource App ReCaptcha Dataset Motivation and Origins
37 Dataset Motivation and Origins Problem: Not enough boxes in existing datasets Solution: Concerted box collection effort (LOCO team) MOST BOXES IN ANY PUBLIC DATASET! Dataset Motivation and Origins
38 Dataset Motivation and Origins su.bo "lucky cats" Problem: Web bias Solution: Dedupe against the web Tomi Tapio K "Staring into the Astral Plane" Brenna "Look at that cute face." Typical "Cat" in Image Search. Dataset Motivation and Origins "Cat" Images in OID dataset. SaraLowgren "DSC00405"
39 Image-Level Annotations Machine Generated Predictions: ICA ENet model Paid Raters: Crowd-compute Crowdsourcing: Crowdsource App, ReCaptcha Winter Snow Sled Sledding Headgear Image-Level Annotations
40 Crowd Compute "Verify Entity" Task Verification of ICA classifier output >20k unique labels Expert raters for fine-grained Crowd Compute "Verify Entity" Task
41 Crowd Sourced Labels Crowdsource App Recaptcha Crowd Sourced Labels go/googlevillage, go/recaptcha
42 Image-Level Annotation Statistics (OID V3) Train Images Test Validation # Classes # Trainable Classes 9M 125K 42K - - Machine Generated Labels 79M 1.5M 512K 8K 5K Human Verified Labels 20M 1.6M 551K 20K 5K Compare to other datasets Imagenet: 14M labels, 22K classes Image-Level Annotation Statistics (OID V3)
43 Annotation Count Image-Level Annotation Statistics (OID V3 Train Set) Negative Verifications Positive Verifications Image-Level Annotation Statistics (OID V3 Train Set)
44 Boxes Freeform box drawing XClick: Faster box drawing method ActiveMIL tvmonitor Jug Bottle Boxes houseplant
45 Extreme Clicking (XClick) Idea: Click physical points on object E.g., leftmost point on the motorcycle Same quality as drawing 7-10s (vs 30s traditional) Extreme Clicking (XClick) go/loco-vision
46 ActiveMIL Verification Sequence ActiveMIL Verification Sequence go/loco-vision
47 ActiveMIL Verification Sequence ActiveMIL Verification Sequence go/loco-vision
48 ActiveMIL Verification Sequence ActiveMIL Verification Sequence go/loco-vision
49 Box Statistics (OID V3) Train Test Validation # Classes # Trainable Classes Images 1.6M 108K 36K - - Boxes 3.7M 625K 205K MOST BOXES IN ANY PUBLIC DATASET! Compare to other datasets Imagenet: 350K* boxes, 200 classes COCO: 600K boxes, 80 classes * Up to 1M boxes if one really digs Box Statistics (OID V3)
50 Box Count Box Statistics (OID V3 Train Set) Box Statistics (OID V3 Train Set)
51 Pre-trained models Image classifiers Available on externally on github Object detection models Available externally via Object Detection API Pre-Trained Models github.com/openimages, github.com/tensorflow/models/tree/master/research/object_detection
52 Next Steps Next Steps More boxes XClick, internally ~9M almost ready More labels Crowdsource Other types of groundtruth Segmentation OCR... Challenge? - We could use 20%ers!
53 Thank you! go/openimages github.com/openimages Thank You!
54 APPENDIX
55 Comparison to Other Datasets Images Open Images V3 Categories Image Annotations Boxes 9M 20K trainable: 5K 20M 3.7M 14M 22K 14M *350K COCO 330K objects: 80 stuff: 91 N/A 600K PASCAL VOC K 20 N/A 27.5K ImageNet * Up to 1M boxes if one really digs COCO: cocodataset.org, ImageNet: image-net.org, PASCAL VOC 2012: host.robots.ox.ac.uk/pascal/voc/voc2012 Comparison to Other Datasets
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