Wow, that looks fantastic! I was looking like something like this for ages.
Some feedback though:
- As an independent developer, pricing is important to me. It was very difficult to find pricing for the Watson APIs (apparently it was in Bluemix?) and if I wasn't a little more determined (thanks to the ability to train my own classifier), I wouldn't have persevered.
- If I already have a wealth of labelled data (I do), it seems difficult to train a new classifier for the Visual Recognition service. If I have 200 000 images each with an average of 20 labels (from a set of ~2000 labels) for example, the positive + negative sample per label is very time and bandwidth consuming, as I have to train 2000 classifiers using ~ 5000 images per classifier (for plenty of training data), for a total of ~10 000 000 uploads. It'd be far nicer to be able to upload a folder of JPEGs with a JSON blob per file containing labels (or a classifier name) and have Watson derive positive and negative samples from it.
As a work-around for mass uploading, I might suggest signing up for a 30 day free Bluemix trial [1]. You could then upload your data to a container and script the creation of sample archives and uploads from there.
The bottom line is - we believe we can provide much better API service with competing level of technology precision.
At the same time we also stress a lot on specific things like custom categorization training and enterprise/on-premise installations (both differ from custom software).
Actually we don't plan to run away in a niche market, though some people suggest it as a proper strategy. We'll give them a good run for their money on the broad use-case.
I believe that the "Hacker" culture works in our favour in this case and I hope you help us prove it :)
Some feedback though:
- As an independent developer, pricing is important to me. It was very difficult to find pricing for the Watson APIs (apparently it was in Bluemix?) and if I wasn't a little more determined (thanks to the ability to train my own classifier), I wouldn't have persevered.
- If I already have a wealth of labelled data (I do), it seems difficult to train a new classifier for the Visual Recognition service. If I have 200 000 images each with an average of 20 labels (from a set of ~2000 labels) for example, the positive + negative sample per label is very time and bandwidth consuming, as I have to train 2000 classifiers using ~ 5000 images per classifier (for plenty of training data), for a total of ~10 000 000 uploads. It'd be far nicer to be able to upload a folder of JPEGs with a JSON blob per file containing labels (or a classifier name) and have Watson derive positive and negative samples from it.