First of, i really appreciate how simple it was to get started with the info in the the README. My background is that I understand the principals of deep networks from theory and have not done anything real with it.
So coming here and with less than one hour work I was able to (on windows) to set up a docker container running chainercv + running a faster rcnn detection on a sample image, was extremely satisfying.
Would it not be possible to author the same experience for a simple training of a small getting started tutoring with own data?
Lets say a guy have 10 images and he takes the time to mask them, put them in a folder structure and then run a simple demo.py script that trains on 9 of them and do a detect on the last. I know this is will yield poor results, but these kind of demos really accelerate the adoption because its something everyone can do and it yield much more value when people can do it on their own use cases instead always the same sample data sets that always works.
if anyone can contribute with a folder structure and demo script to train, i can write up the tutorial and documentation to help peolpe
Hello
First of, i really appreciate how simple it was to get started with the info in the the README. My background is that I understand the principals of deep networks from theory and have not done anything real with it.
So coming here and with less than one hour work I was able to (on windows) to set up a docker container running chainercv + running a faster rcnn detection on a sample image, was extremely satisfying.
Would it not be possible to author the same experience for a simple training of a small getting started tutoring with own data?
Lets say a guy have 10 images and he takes the time to mask them, put them in a folder structure and then run a simple demo.py script that trains on 9 of them and do a detect on the last. I know this is will yield poor results, but these kind of demos really accelerate the adoption because its something everyone can do and it yield much more value when people can do it on their own use cases instead always the same sample data sets that always works.
if anyone can contribute with a folder structure and demo script to train, i can write up the tutorial and documentation to help peolpe