Per-unit visualizations are included for the caffenet-yos network but not for other networks (the total size is at least several GB, which becomes cumbersome to distribute).
But the per-unit visualizations can be computed for any network:
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To find synthetic images that cause high activation via regularized optimization, use the optimize_image.py script. Script usage is explained here.
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To find images (for FC layers) or crops (for conv layers) from a set of images (e.g. the ImageNet training or validation set) that cause highest activation, use the find_max_acts.py script to go through the set of images and note the top K images/crops and then crop_max_patches.py to use the noted max images / max locations to output the crops and/or deconv of the crops.
Results of both of the above steps will be saved as per-unit jpg image files, which can be loaded by the toolbox when browsing units. To do so, just point the caffevis_unit_jpg_dir setting to the directory containing the per-unit images.