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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
by 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 agoFor 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.