4 ms·

One thing that really puts me off Tensorflow is the large amount of work you have to do just to get your data in. I recently trained a detector using Nvidia's

by joshvm 9y ago

One thing that really puts me off Tensorflow is the large amount of work you have to do just to get your data in. I recently trained a detector using Nvidia's detectnet and the DIGITS environment really makes it easy from dataset/database creation to model generation and training. The web interface handles all the padding and resizing for you when you import your images, and detectnet does online augmentation during training. It takes data in KITTI format which is just image + textfile with labels. No wrangling in Python needed. The documentation is pretty terrible though, you have to pore over github issues to fix and modify things, but it does work.

deleted 9y ago
[deleted]
zeryx 9y ago
I would be lying if I didn't agree with you. The valuable part is that Tensorflow's extendability and functionality are best in class. Making an object detector in scikit-learn is incredibly difficult, same with torch. Keras definitely can improve the experience but keras is also quite fragile. When in doubt, use the best tool for the job.
- joshvm 9y ago
  For what I was using it for - locating fruit on a plant - detectnet works pretty well (it runs at 7fps on a TX2 at 1280x960). It's a modified GoogLeNet with some custom layer on the end to do the bounding boxes. DIGITS itself is just a management console. That said, you can train Tensorflow models using DIGITS, so I assume that DIGITS handles the data IO for you: https://github.com/NVIDIA/DIGITS/blob/master/docs/GettingStartedTensorflow.md https://github.com/NVIDIA/DIGITS/blob/master/docs/GettingSta... It looks like you just define a wrapper around your model so that DIGITS knows how to run it and evaluate the loss, but otherwise your model could be anything.
malux85 9y ago
Shameless plug: our Deep Learning platform SignalBox helps solve this problem, it’s a GUI for TensorFlow with a drag and drop neural net editor. We’re still growing and iterating the platform rapidly based on customer feedback, feel free to reach out
kakaorka 9y ago
I participated in a closed source project that tackled exactly this. We added tensorflow support into DIGITS and it made it a much easier framework to use.

by joshvm 9y ago

deleted 9y ago

[deleted]

zeryx 9y ago

I would be lying if I didn't agree with you. The valuable part is that Tensorflow's extendability and functionality are best in class. Making an object detector in scikit-learn is incredibly difficult, same with torch. Keras definitely can improve the experience but keras is also quite fragile. When in doubt, use the best tool for the job.

joshvm 9y ago
For what I was using it for - locating fruit on a plant - detectnet works pretty well (it runs at 7fps on a TX2 at 1280x960). It's a modified GoogLeNet with some custom layer on the end to do the bounding boxes. DIGITS itself is just a management console. That said, you can train Tensorflow models using DIGITS, so I assume that DIGITS handles the data IO for you: https://github.com/NVIDIA/DIGITS/blob/master/docs/GettingStartedTensorflow.md https://github.com/NVIDIA/DIGITS/blob/master/docs/GettingSta... It looks like you just define a wrapper around your model so that DIGITS knows how to run it and evaluate the loss, but otherwise your model could be anything.

malux85 9y ago

Shameless plug: our Deep Learning platform SignalBox helps solve this problem, it’s a GUI for TensorFlow with a drag and drop neural net editor. We’re still growing and iterating the platform rapidly based on customer feedback, feel free to reach out

kakaorka 9y ago

I participated in a closed source project that tackled exactly this. We added tensorflow support into DIGITS and it made it a much easier framework to use.