[MGNLPER-17] Recognize typical marketing images reliably Created: 26/Mar/18 Updated: 07/May/19 Resolved: 26/Oct/18 |
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| Status: | Closed |
| Project: | Periscope |
| Component/s: | None |
| Affects Version/s: | None |
| Fix Version/s: | 1.0 |
| Type: | Task | Priority: | Neutral |
| Reporter: | Antti Hietala | Assignee: | Cedric Reichenbach |
| Resolution: | Done | Votes: | 0 |
| Labels: | None | ||
| Remaining Estimate: | Not Specified | ||
| Time Spent: | Not Specified | ||
| Original Estimate: | Not Specified | ||
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| Epic Link: | Periscope back-end MVP | ||||||||||||||||
| Sprint: | Basel 158 | ||||||||||||||||
| Story Points: | 5 | ||||||||||||||||
| Description |
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User story:
Internal story:
Business benefit / value: Reliable and accurate local image recognition makes a great first impression. It convinces evaluators. Background: Local image recognition is currently limited to ImageNet 1000 synsets (synonym sets). This collection of labels does not represent typical marketing imagery. It is heavily biased towards animals ("African elephant", "hyena", "weasel") while common marketing subjects like "computer", "person" and "shoe" are missing. This means that a neural network pre-trained on Imagenet 1000 classifications does not recognize common marketing subjects. Acceptance criteria:
Implementation proposal (optional, up to PD to decide):
Attachments:
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| Comments |
| Comment by Antti Hietala [ 12/Oct/18 ] |
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Timebox research to 5 SP |
| Comment by Cedric Reichenbach [ 26/Oct/18 ] |
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Outcome after timeboxed effort: There are no useful-enough pretrained networks available. The most useful one is probably TinyYOLO, which is pretty accurate and detects classes like "person" or "car", but still only supports 20 different classes. Also, it also detects (boundling-box) object locations, which we don't need here and just causes additional computation cost. Here a quick draft integration: https://git.magnolia-cms.com/users/creichenbach/repos/image-recognition/commits?until=refs%2Fheads%2FMGNLPER-17-more-relevant-tags&merges=include See the linked follow-up issue for more infos about how to potentially proceed with a customly trained network. |