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This is great for making new models, but we also get the pre-trained models of ,keras,.applications (also seen elsewhere). This model can take input images that are 224 pixels on a side, so we have to make our image that size. It's pretty fun that this kind of super-easy access to quite good pre-trained models is now available all within the TensorFlow package. The thousand ImageNet categories ...
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Introduction. In this post we want to present Our Image Segmentation library that is based on Tensorflow and TF-Slim library, share some insights and thoughts and demonstrate one application of Image Segmentation.. To be more precise, we trained FCN-32s, FCN-16s and FCN-8s models that were described in the paper “Fully Convolutional Networks for Semantic Segmentation” by Long et al. on ...
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Easy, huh? A lot of things look easy on the surface, but as soon as we impose certain constraints on them, things become pretty hard. For example, the Jarvis March algorithm described in the video has complexity O(nh) where n is the number of input points and h is the number of points in the convex hull.Chan’s algorithm has complexity O(n log h).. Is an O(n) algorithm possible?