30th April 2018 new version of Open Images Dataset V4 is released. There is also announced a challenge for best object detection results using this dataset.
Here you can see data examples: Open Images Dataset V4
During ECCV 2018 conference there will be a workshop dedicated Open Images Challenge (presented by Vittorio Ferrari, Alina Kuznetsova, Jasper Uijlings, Rodrigo Benenson, Victor Gomes, Matteo Malloci). They will announce challenge results.
The Challenge has two tracks:
There are image-level labels (table 1) and bounding boxes for object detection (table 2). Labels where generated automatically by machine and then checked by humans thanks to Crowdsource labeler.
Table 1: Image-level labels
| Train | Validation | Test | # Classes | # Trainable Classes | |
|---|---|---|---|---|---|
| Images | 9,011,219 | 41,620 | 125,436 | – | – |
| Machine-Generated Labels | 78,977,695 | 512,093 | 1,545,835 | 7,870 | 4,764 |
| Human-Verified Labels | 27,894,289 pos: 13,444,569 neg: 14,449,720 |
551,390 pos: 365,772 neg: 185,618 |
1,667,399 pos: 1,105,052 neg: 562,347 |
19,794 | 7,186 |
Table 2: Object Detection track annotations on training set
| Classes | Images | Image-Level Labels | Bounding boxes | |
|---|---|---|---|---|
| Train | 500 | 1,743,042 | 5,743,460 pos: 3,830,005 neg: 1,913,455 |
12,195,144 |
Here are some examples, more you can find here: Open Images Dataset V4
1 marca, 2019 o 7:23 am
Thank you for sharing these information.
Is there any source code for data loading?
1 marca, 2019 o 8:59 am
Please try this toolkit: https://github.com/EscVM/OIDv4_ToolKit
I guess it is a good idea to create a post on how to use the data, am I right?
What’s your use case? or you just wanted to learn?
Dodaj komentarz