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Author here, if you have any questions/comments I'll check back in periodically and answer them here!

by zeryx 9y ago

Q6T46nT668w6i3m 9y ago
Hi, zeryx: How does this work in production? I'm especially curious about the stuff that isn't part of the TensorFlow graph like anchor generation and post-processing. Is your service running the graph directly or does it run some Python script that executes the graph?
- zeryx 9y ago
  So the object detection extension is mostly focused on breaking down complex operations like anchor generation into functional steps that are compatible with the base tensorflow metagraph definition. You can see these builders here: https://github.com/tensorflow/models/tree/master/research/object_detection/anchor_generators https://github.com/tensorflow/models/tree/master/research/ob... What that means is that when it comes to inference in a production environment, we only need our Tensorflow python package, as the metagraph is defined in terms that the base Tensorflow package can decypher. Although as I'm not an author of the object detection API, there is probably a more nuanced answer here.
AndrewKemendo 9y ago
1. Assuming someone is looking for only one or a handful of objects, why would they train their own dataset on open image instead of using the inception/object_detection built into TF? Seems like this use case is for systems that are looking to eval/classify a lot of different object classes. 2. Why would someone run this on Algorithmia instead of their own hosted NC/P2/P3 instance? 3. Have you tried open image with any other DNNs like Shang's implementation of FastMaskRCNN?
- deleted 9y ago
  [deleted]
- zeryx 9y ago
  1) The biggest reason is label diversity. COCO is a great dataset, but at 90 labels you're going to be missing some potential. The problem with commercial machine learning development is access to data, and with Open Images and it's 545 classification labels we get that diversity. 2) Scaling is hard, Cross-language Interoperability is hard, And bringing your product to market is hard. Algorithmia solves all of those problems with grace and has a development experience that is second-to-none. Don't take my word for it, compare it against tensorflow-serving other competitor technologies. 3) I haven't yet but I could certainly see an improvement in performance, we did notice a dramatic difference between our SSD and faster-rcnn model as was expected. This was only an intro, but if you create a model that performs better than ours, we'd love to host it on Algorithmia.
  - krasin 9y ago
    A small elaboration. OpenImages v2 has 545 classes for object detection, and 5000 classes for image classification.
    - zeryx 9y ago
      Thanks, it's worth mentioning that we're entirely focused on the dataset for object detection here, but it has a massive classification dataset as well.
SloopJon 9y ago
I can't quite parse this sentence: "The Open Images dataset is comprehensive and large, but many of its classes are unbalanced which effects or precision of underrepresented classes." Do you mean "affects our"?
- zeryx 9y ago
  Yea sorry that was a mobile fix, thanks for catching that. I'll update shortly.
Omnipresent 9y ago
Hi On this demo image [0], why isn't "text" found as well since there is clearly text in the image. Is it because "text" isn't considered an object? If so, what would it take (based on your article) to also train for text? Thanks! [0] https://i.imgur.com/0jwgJAG.png https://i.imgur.com/0jwgJAG.png

Author here, if you have any questions/comments I'll check back in periodically and answer them here!

by zeryx 9y ago

Author here, if you have any questions/comments I'll check back in periodically and answer them here!

Hi, zeryx: How does this work in production? I'm especially curious about the stuff that isn't part of the TensorFlow graph like anchor generation and post-processing. Is your service running the graph directly or does it run some Python script that executes the graph?

zeryx 9y ago
So the object detection extension is mostly focused on breaking down complex operations like anchor generation into functional steps that are compatible with the base tensorflow metagraph definition. You can see these builders here: https://github.com/tensorflow/models/tree/master/research/object_detection/anchor_generators https://github.com/tensorflow/models/tree/master/research/ob... What that means is that when it comes to inference in a production environment, we only need our Tensorflow python package, as the metagraph is defined in terms that the base Tensorflow package can decypher. Although as I'm not an author of the object detection API, there is probably a more nuanced answer here.

AndrewKemendo 9y ago

1. Assuming someone is looking for only one or a handful of objects, why would they train their own dataset on open image instead of using the inception/object_detection built into TF? Seems like this use case is for systems that are looking to eval/classify a lot of different object classes. 2. Why would someone run this on Algorithmia instead of their own hosted NC/P2/P3 instance? 3. Have you tried open image with any other DNNs like Shang's implementation of FastMaskRCNN?

deleted 9y ago
[deleted]
zeryx 9y ago
1) The biggest reason is label diversity. COCO is a great dataset, but at 90 labels you're going to be missing some potential. The problem with commercial machine learning development is access to data, and with Open Images and it's 545 classification labels we get that diversity. 2) Scaling is hard, Cross-language Interoperability is hard, And bringing your product to market is hard. Algorithmia solves all of those problems with grace and has a development experience that is second-to-none. Don't take my word for it, compare it against tensorflow-serving other competitor technologies. 3) I haven't yet but I could certainly see an improvement in performance, we did notice a dramatic difference between our SSD and faster-rcnn model as was expected. This was only an intro, but if you create a model that performs better than ours, we'd love to host it on Algorithmia.
- krasin 9y ago
  A small elaboration. OpenImages v2 has 545 classes for object detection, and 5000 classes for image classification.
  - zeryx 9y ago
    Thanks, it's worth mentioning that we're entirely focused on the dataset for object detection here, but it has a massive classification dataset as well.

SloopJon 9y ago

I can't quite parse this sentence: "The Open Images dataset is comprehensive and large, but many of its classes are unbalanced which effects or precision of underrepresented classes." Do you mean "affects our"?

zeryx 9y ago
Yea sorry that was a mobile fix, thanks for catching that. I'll update shortly.

Omnipresent 9y ago

Hi On this demo image [0], why isn't "text" found as well since there is clearly text in the image. Is it because "text" isn't considered an object? If so, what would it take (based on your article) to also train for text? Thanks! [0] https://i.imgur.com/0jwgJAG.png https://i.imgur.com/0jwgJAG.png