One of the best things about ImageMonkey is our tight integration with Machine learning frameworks like Tensorflow and Mask RCNN.
You want to train your own image classifier based on the ImageMonkey dataset?
It's as easy as that:
CPU version:
docker pull bbernhard/imagemonkey-train:latest
docker run -it bbernhard/imagemonkey-train:latest
GPU version:
docker pull bbernhard/imagemonkey-train:latest-gpu
docker run --runtime=nvidia -it bbernhard/imagemonkey-train:latest-gpu
This will download and run a docker image where we've already configured tensorflow and MaskRCNN to work seamlessly with ImageMonkey.
After you've started the docker container, use the monkey script to interact with ImageMonkey dataset.
Here's a list of all available commands:
root@ecebfa2aea35:/# monkey --help
usage: PROG [-h] {train,list-labels} ...
positional arguments:
{train,list-labels}
train train your own model
list-labels list all labels that are available at ImageMonkey
optional arguments:
-h, --help show this help message and exit
Let's assume you want to train your image classifier on all images that are labeled with dog or cat. Then simply run
monkey train --labels="cat|dog" --type="image-classification"
lean back and go get yourself a coffee ;-)
The script automatically downloads all ImageMonkey images that are labeled with dog or cat and uses transfer learning to retrain an
existing image classifier (inception v3) on these labels.
Umm, wait, I don't want to train an image classifier - I rather want to train a neural net that is able to do object segmentation.
Nothing easier than that. Simply change the type
monkey train --labels="cat|dog" --type="object-segmentation"
and MaskRCNN will be used instead.
In case you want to access ImageMonkey via Python, have a look at our Python library. But keep in mind, the library is still in an alpha stage and the API might change at any point!