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Real-time image saliency 🌠 (NIPS 2017)
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Real-time image saliency

See what your classifier is looking at! [PAPER]


Real-time saliency view

Run python to perform the saliency detection on the video feed from your webcam. You can choose the class to visualise (1000 ImageNet classes) as well as the confidence level - low confidence will highlight anything that resembles or is related to the target class, while higher confidence will only show the most salient parts.

The model runs on a CPU by default and achieves about 5 frames per second on my MacBook Pro (and over 150 frames per second on a GPU).


Run python to start the training. By default it will train the model to perform the saliency detection on the ImageNet dataset for the resnet50 classifier, but you can choose your own dataset/classifier combination. You will need PyTorch wich cuda support, the training will be performed on all your GPUs in parallel. I also advide to run the script from iTerm 2 terminal so that you can see the images during traning.


Using pretrained model

from saliency_eval import get_pretrained_saliency_fn

sal_fn = get_pretrained_saliency_fn()

# get the saliency map (see get_pretrained_saliency_fn doc for details)
sal_map = sal_fn(images, selectors)


pip install -r requirements.txt

Also, in case you don't have OpenCV3 installed run:

pip install opencv-contrib-python

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