Not exactly. The problem is that different models are tuned for different use cases. So I'd expect something like this:
$ pip install pymodel-ResNet-1337
#! /usr/bin/env python3
import ai
model = ai.loadModel("ResNet-1337",input=ai.input.Image,output=ai.output.Category)
model.guess(open('myimage.png'))
Problems abound, for example, how would you resample/rescale the image.
I'd want, to start, a standard model format that can be serialized/deserialized into any language that can be (for example) pip installed and loaded. People seem to use HD5 but I don't think there is any sort of "standard".
So I'd expect the first incarnations of this idea to look like this:
$ pip install tfmodel-ResNet-1377
or
$ pip install kerasmodel-ResNet-1337
With some hooks for loading models:
#!/usr/bin/env python3
# whereas before you'd build a network, train it, and then use it, here you get the whole shebang in one go
model = keras.loadInstalledModel("ResNet-1337")
I'd want, to start, a standard model format that can be serialized/deserialized into any language that can be (for example) pip installed and loaded. People seem to use HD5 but I don't think there is any sort of "standard".
So I'd expect the first incarnations of this idea to look like this:
or With some hooks for loading models: Rest of it is up to the user, as usual