quantization

问题 A Note For Readers: This is a long question, but it needs a background to understand the question asked. The color quantization technique is commonly used to get the dominant colors of an image. One of the well-known libraries that do color quantization is Leptonica through the Modified Median Cut Quantization (MMCQ) and octree quantization (OQ) Github's Color-thief by @lokesh is a very simple implementation in JavaScript of the MMCQ algorithm: var colorThief = new ColorThief(); colorThief

问题 On emulators running Android 4.0 or 4.0.3, I am seeing horrible colour banding which I can't seem to get rid of. On every other Android version I have tested, gradients look smooth. I have a SurfaceView which is configured as RGBX_8888, and the banding is not present in the rendered canvas. If I manually dither the image by overlaying a noise pattern at the end of rendering I can make the gradients smooth again, though obviously at a cost to performance which I'd rather avoid. So the banding

问题 I am trying to quantize an image into 10 colors in C# and I have a problem in draw the quantized image, I have made the mapping table and it is correct, I have made a copy of the original image and I am changing the color of pixels based on the mapping table , I am using the below code: bm = new Bitmap(pictureBox1.Image); Dictionary<Color, int> histo = new Dictionary<Color, int>(); for (int x = 0; x < bm.Size.Width; x++) for (int y = 0; y < bm.Size.Height; y++) { Color c = bm.GetPixel(x, y);

问题 I did quantization with tensorflow following this manual: https://www.tensorflow.org/versions/master/how_tos/quantization/index.html But when I load my quantizied graph and do something like: with tf.Session() as session: tf.initialize_all_variables().run() I get the error: --------------------------------------------------------------------------- NotFoundError Traceback (most recent call last) <ipython-input-55-b0945a14b01e> in <module>() 5 all_steps = 0 6 with tf.Session() as session: ----

问题 I have following code that was given to me, but I am not sure at all as to what the logic here is. The idea, I believe, is that this will histogram/quantize my data. Here is the code: The input: x = 180.*rand(1,1000); %1000 points from 0 to 180 degrees. binWidth = 20; %I want the binWidth to be 20 degrees. The main function: % ------------------------------------------------------------------------- % Compute the closest bin center x1 that is less than or equal to x % ------------------------

问题 I am working to quantize my existing inception model graph in an attempt to reduce its size from ~89mb so something around 30mb as claimed according to the google tutorial here. The issue I am having is when I try to copy the following code snippet into mac OS terminal I get the following error. Code Snippet I try to copy and run: bazel build tensorflow/tools/graph_transforms:transform_graph bazel-bin/tensorflow/tools/graph_transforms/transform_graph \ --in_graph=/tmp/classify_image_graph_def

问题 I have trained an object detection model. Now I'm trying to speed it up for inference using quantization provided by Tensorflow Lite graph converter. But when I call the tf.lite.TFLiteConverter.from_frozen_graph method, I am running into an error. I have also found a similar, unanswered question asked almost a year ago and I was wondering if TFLite's support has improved now. Here is what I'm calling: converter = tf.lite.TFLiteConverter.from_frozen_graph( model_path, input_arrays = ['input_1'

问题 I have been trying to perform quantization aware training for deeplab using the guide given in this link https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/quantize However, I am not sure where exactly to put the below 2 lines that are required to activate the quantization g = tf.get_default_graph() tf.contrib.quantize.create_training_graph(input_graph=g,quant_delay=2000000) Where exactly in the deeplab train.py file do I put the above two lines? I already tried on line

问题 I am reading about Product Quantization, from section II.A page 3 of PQ for NNS, that says: ..all subquantizers have the same finite number k* of reproduction values. In that case the number of centroids is (k*)^m where m is the number of subvectors. However, I do not get k* at all! I mean in vector quantization we assign every vector to k centroids. In produce quantization, we assign every subvector to k centroids. How did k* come into play? 回答1: I think k* is the number of centroids in each

问题 In Product Quantization for Nearest Neighbor Search, when it comes to section IV.A, it says they they will use a coarse quantizer too (which they way I feel it, is just a really smaller product quantizer, smaller w.r.t. k , the number of centroids). I don't really get why this helps the search procedure and the cause might be that I think I don't get the way they use it. Any ides please ? 回答1: As mentioned in the NON EXHAUSTIVE SEARCH section, Approximate nearest neighbor search with product