The deep learning revolution has brought significant advances in a number of fields , primarily linked to image and speech recognition. The standardization of image classification tasks like the ImageNet Large Scale Visual Recognition Challenge  has resulted in a reliable way to compare top performing architectures.
This Docker container contains the tools to train an image classifier on your custom dataset. It is a highly customizable tool that let's you choose between tens of different top performing arquitectures and training parameters.
: Yann LeCun, Yoshua Bengio, and Geofrey Hinton. Deep learning. Nature, 521(7553):436–444, may 2015.
: Olga Russakovsky et al. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211–252, 2015.
Run locally on your computer
You can run this model directly on your computer, assuming that you have Docker installed, by following these steps:
$ docker pull deephdc/deep-oc-image-classification-tf $ docker run -ti -p 5000:5000 deephdc/deep-oc-image-classification-tf
If you do not have Docker available or you do not want to install it, you can use udocker within a Python virtualenv:
$ virtualenv udocker $ source udocker/bin/activate $ git clone https://github.com/indigo-dc/udocker $ cd udocker $ pip install . $ udocker pull deephdc/deep-oc-image-classification-tf $ udocker create deephdc/deep-oc-image-classification-tf $ udocker run -p 5000:5000 deephdc/deep-oc-image-classification-tf
Once running, point your browser to
and you will see the API documentation, where you can test the model
functionality, as well as perform other actions (such as training).
For more information, refer to the user documentation.