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I strongly believe that if you had the right teacher you could master computer vision and deep learning.ĭo you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or has to involve complex mathematics and equations? Or requires a degree in computer science?Īll you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. Grant and T-Rex have morphed into something else entirely. Grant has developed a severe case of dog-ass. Figure 6: This one I found amusing - it seems that Dr. Figure 5: The same goes for the inception_3b/3x3_reduce layer. Figure 4: The inception_3a/3×3 layer also products a nice effect. The lower layers of the network reflect edge-like regions in the input image. In the meantime, here are some of my favorite layers: Figure 3: This is my far my favorite one of the bunch. gif is pretty large at 9.6mb, so give it a few seconds to load, especially if you are on a slow connection. gif of all layer visualizations below: Figure 2: Visualizing every layer of GoogLeNet using bat-country. I generated my results on an Amazon EC2 g2.2xlarge instance with GPU support enabled so the script finished up within 30 minutes. base-model $CAFFE_ROOT/caffe/models/bvlc_googlenet \Īnd the visualization process will kick off. I then executed the Python script using the following command: $ python visualize_layers.py \ This script requires three command line arguments: the -base-model directory where our Caffe model lives, the path to our input -image, and finally the -output directory where our images will be stored after being passed through the network.Īs you’ll also see, I am using a try/except block to catch any layers that cannot be used for visualization.īelow is the image that I inputted to the network: Figure 1: The iconic input image of Dr. Print(" processing layer ``".format(layer)) # perform visualizing using the current layer # extract the filename and extension of the input imageįilename = args.image # filter warnings, initialize bat country, and grab the layer names of # construct the argument parser and parse the argumentsĪp.add_argument("-b", "-base-model", required=True, help="base model path")Īp.add_argument("-i", "-image", help="path to image file")Īp.add_argument("-o", "-output", help="path to output directory")
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Looking for the source code to this post? Jump Right To The Downloads Section Visualizing every layer of GoogLeNet with Pythonīelow follows my Python script to load an image, loop over every layer of the network, and then write each output image to file: # import the necessary packages