##A tensorflow implementation of Fully Convolutional Networks For Semantic Segmentation.
To run the trained classifier on some images:
python wrap_pixelwise.py run COCO 7 pix7 -imgdir=<directory>
This will iterate over images in <directory> and using a model trained on 72 known classes...
- save visualizations of the predictions in the demo-results directory. These start with the prefix 'net-'.
- save further visualizations in the demo-results direcotry showing a breakdown by which layers the predictions are coming from (recall that in the paper, the final predictions are a weighted average of predictions from a few layers: the earlier layers having higher resolution and the later layers having higher-level features). These start with the prefix 'comparison-'.
- print out the top-k categories associated with an image along with the name of the image.
None is a category because this model has been trained to distinguish foreground from background.
The only software dependencies are the various python modules being imported and CUDA. It is tested on CUDA 7.5 and with python 3.5. In terms of hardware, a GPU will help, but tensorflow should be able to failover into CPU-only mode.