7 ms·
Supercharge your Computer Vision models with the TensorFlow Object Detection API
- aw3c2 9y agoDirect link https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html https://research.googleblog.com/2017/06/supercharge-your-com...
- artursapek 9y agoAdmins update submission please
- impish19 9y agohttps://news.ycombinator.com/item?id=14562314 https://news.ycombinator.com/item?id=14562314
- sharemywin 9y agoThey need some kind of context input. -GPS position, intent/goal, domain etc. I'm at a dog show I would want breed etc. I'm on the street I just want it come back dog maybe dangerous dog, friendly dog. Also, would be cool/scary to just get back movable object 1, person 1, living movable object 3 etc. and if I give it multiple scenes from a video it knows person 1 is the same person 1 and if I name (them) Tony it keeps tracking tony.
- neuronexmachina 9y agoI imagine you could use the confidence-value output of the object-detection API as input into a separate system that would also incorporate the other inputs you mention.
- asciimo 9y ago> I'm on the street I just want it come back dog maybe dangerous dog, friendly dog. Most autonomous humans ship with this capability.
- bbrian 9y agoBut not all. https://www.google.com/search?q=blind+man+bitten+by+dog https://www.google.com/search?q=blind+man+bitten+by+dog
- accountyaccount 9y agoThis would be great to run a security camera still feed through. It could completely eliminate false positives.
- kyrra 9y agoI wonder if Nest is using it with their new cam[0], as it has person alerts now (with face detection). [0] https://nest.com/camera/meet-nest-cam-iq/ https://nest.com/camera/meet-nest-cam-iq/
- halflings 9y agoThey would probably use FaceNet[0] then, if they only want to detect faces, as that should give better results. [0] https://arxiv.org/abs/1503.03832 https://arxiv.org/abs/1503.03832
- odbol 9y agoExcept they wouldn't be able to detect people wearing masks, which is probably an important thing for a security camera to do...
- spullara 9y agoIn the research blog entry they do say they are using it in the new Nest cams. https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html https://research.googleblog.com/2017/06/supercharge-your-com...
- jd20 9y agoWe still have a ways to go, to completely eliminating false positives, but these tools will help us get there. For example, you can recognize different types of objects now but we still need to figure out which are meaningful or not (like a person or animal vs a tree blowing in the wind). Even certain classes, some are benign while others or not, for example pedestrians walking by the front of my house versus a guy wearing ski mask fiddling with my window, they're both people, but their behavior is what separates them.
- elliottcarlson 9y agoSo, could you use this to solve the image recognition captcha's that ask you to select all images that contain [object]?
- glup 9y agoMaybe they will make you do a Captcha before you access the API?
- gregable 9y agoLOL. The API requires you to first identify all of the objects in a different picture.
- andrewrice 9y agoThat's how they train the API!
- EGreg 9y agoSo then you bootstrap that using another api key :)
- polskibus 9y agoIs this a new Google API for use through their cloud offering or is it a set of tensorflow artifacts one can download and use freely without ever contacting Google Cloud?
- matt4077 9y agoYes. Both.
- spullara 9y agoIt has been added to the TensorFlow github repository like Inception. You can use it completely independently from Google. https://github.com/tensorflow/models/tree/master/object_detection https://github.com/tensorflow/models/tree/master/object_dete...
- azernik 9y agoClicking through the two layers of links, it is a GitHub repository containing pre-trained models, training scripts, and scripts for running the models on Google Cloud: https://github.com/tensorflow/models/tree/master/object_detection https://github.com/tensorflow/models/tree/master/object_dete...
- radarsat1 9y agoHoly moly, I can't believe I didn't know about https://github.com/tensorflow/models https://github.com/tensorflow/models
- matt4077 9y agoFinally I'm getting the results for all those traffic sign CAPTCHAS I've been solving. (And I just noticed I should not have include the post as part of the sign–sorry for any inaccuracies I may have caused)
- deleted 9y ago[deleted]
- koolba 9y agoAnyone know of a sample app that uses this? Say to detect if something is or isn't a hot dog?
- fosk 9y agoYes, here you go: https://itunes.apple.com/us/app/not-hotdog/id1212457521 https://itunes.apple.com/us/app/not-hotdog/id1212457521
- etaioinshrdlu 9y agohttps://nothotdog.io https://nothotdog.io
- Drdrdrq 9y agoI can't find the license, anyone have better luck?
- advisedwang 9y agoThe root of the repo has Apache license 2.0.
- nostrademons 9y agoBasically everything Google releases is Apache 2.0. It was company policy when I was there.
- zitterbewegung 9y agoSo they are launching all of these frameworks targeted to mobile but what's happening to Tensorflow Lite ? I'm beginning to think that these things that they are releasing are scaffolding for this . I really hope it's not going to be vaporware from google I/O
- dgacmu 9y agoIt's not vaporware. (It's not released yet, but it's not vaporware.) (blah blah this is not an official statement blah blah)
- wyldfire 9y agoI missed I/O -- what's particular to Tensorflow Lite? Is that distinct from the CPU target?
- kyrra 9y agoMobile focused version of tensorflow.
- haimez 9y agoLol. Parallel data computations across resource (including battery) constrained devices? Good news, the owner of the device is now the product. The device is also the product. Can't wait.
- oh_sigh 9y agoI'm going to guess that Google knows a thing or two about mobile devices and their performance characteristics. Also, feeding something through an already trained NN can be pretty darn performant. I'll wait and see what this ends up looking like, but I am hopeful.
- wyldfire 9y agoMany SoCs have under-utilized DSPs that can be used for tensorflow. E.g. https://www.qualcomm.com/news/onq/2017/01/09/tensorflow-machine-learning-now-optimized-snapdragon-835-and-hexagon https://www.qualcomm.com/news/onq/2017/01/09/tensorflow-mach...
- mlaretallack 9y agoJust spent the last 6 months making anpr camera. Now just need to put Python on it. Fun times.
- TuringNYC 9y agoMy sentiment exactly. For my full-time startup, we've been trying, testing (many), and productionizing (one) object detection network for the past nine months. It was a tedious effort of implementing papers from last year's CVPR conference. This makes some of our MOJO go away, but in the scheme of things we can focus more closely on our business. Mixed bag.
- Omnipresent 9y agoWould it be able to detect textual regions in an image as it depics kite/persons in the example image?
- vivekrathod 9y agoYes, if you train those models using a dataset with box annotations. A more relevant model if you want to transcribe the text : https://github.com/tensorflow/models/tree/master/attention_ocr https://github.com/tensorflow/models/tree/master/attention_o...
- yamaneko 9y agoTheir repository is pretty neat! It includes three state-of-the-art architectures in object detection: Faster-RCNN, RFCN, and SSD. It is missing YOLO [1][2], though, which shares some similarities with SSD. Another detector is the recently released Mask-RCNN [3], which of course wouldn't be possible to be included in this publication as we can't travel through time yet. [1]: https://arxiv.org/abs/1506.02640 https://arxiv.org/abs/1506.02640 [2]: https://arxiv.org/abs/1612.08242 https://arxiv.org/abs/1612.08242 [3]: https://arxiv.org/abs/1703.06870 https://arxiv.org/abs/1703.06870
- mrplank 9y agoThere are already newer versions, Yolov2 and DSSD. See http://github.com/sbrugman/deep-learning-papers http://github.com/sbrugman/deep-learning-papers In practice Faster R-CNN worked better for me than YOLOv2 as it, in contrast to what is reported in the paper, had a higher recall for the detect task I used it for.
- pveierland 9y ago"Speed/accuracy trade-offs for modern convolutional object detectors" seems to establish that Faster R-CNN beats R-FCN and SSD-type architectures in accuracy, however YOLOv2 can beat Faster R-CNN and R-FCN in speed, while retaining high accuracy.
- Joboman555 9y agoAnyone know what license this is under?
- deleted 9y ago[deleted]
- nzjrs 9y agoWhat's the hype here. It's a curated model zoo, or?
- pveierland 9y agoThe researchers have created a framework for object detection such that one can easily experiment with using different feature extraction networks, separated from the "meta-architecture" such as Faster R-CNN, R-FCN, or SSD, used to handle the object detection task. They compare many models using this framework, described in https://arxiv.org/abs/1611.10012 https://arxiv.org/abs/1611.10012 - and they were able to construct the winning entry of the COCO 2016 detection challenge based on this research.
- deleted 9y ago[deleted]
- throwaway321373 9y agoThis doesn't seem to include training scripts ?
- monkeydust 9y agoI recently came across a company that's built a ML model to track feet (for footfall observations). It seems that if you had an appropriate training set (labelled feet) you could re-create what they have done with this technology. Perhaps not achieving state-of-art but close. Thoughts?