object-detection

问题 I have read tutorials on how to train a new class using TensorFlow Object Detection API. But what I want to do is to add a new class to the already trained classes of a pre-trained model. For example : MS-COCO pre-trained model has 90 classes. I want to add one more class and detect objects for 91 classes. 回答1: Tensorflow object detection API supports starting from a pre-trained MS COCO checkpoint. Just set fine_tune_checkpoint: "/usr/home/username/tmp/model.ckpt-#####" from_detection

问题 I have a question about objects matching with OpenCV. I'm useing SURF algorithm implemented in opencv 2.3 to first detect features on each image, and then extracting the descriptors of these features. The problem in matching using Brute Force Matcher, I don't know how I judge that the two images are matched or not that's as when I'm using two different images there are lines between descriptors in the two images! These outputs of my code, either the two images -I compare with them - are

问题 Hi i have build my CNN with two classes dogs and cats, i have trained this and now i am able to classify dog and cat image. But what about if i want to introduce a class for new unclassified object? For example if i feed my network with a flower image's the network give me a wrong classification. I want to build my network with a third class for new unclassified object. But how can i build this third class. Which images i have to use to get class for new object that are different from dogs or

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed last year . Background For my final project at university, I'm developing a vehicle license plate detection application. I consider myself an intermediate programmer, however my mathematics knowledge lacks anything above secondary school, which makes producing the right formulas harder than it

问题 i want to detect logo inside image in order to remove it , i have an idea that's to look for objects which have the big number of pixels then remove , another idea is to loop through all the white pixels(i have inverted my image) and look for pixels which forms a large region and then remove this region, is there's any algorithm better that this one , also which methods in opencv will help me to detect object of large pixels number. 回答1: I have a method to do this. I don't know whether this

问题 Im trying to detect objects in realtime using tensorflow. . I ran jupyter notebook in object_detection directory. then I opened the notebook file. It is firing the following error Im getting the following error --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-7-956de605e8fe> in <module>() ----> 1 from utils import label_map_util 2 3 from utils import visualization_utils as vis_util C:\Users\Documents

问题 Assume that I have two arrays of boxes, each of which has the shape (?, b1, 4) and (?, b2, 4) respectively (treat ? as a unknown batch size): box1: [[[1,2,3,4], [2,3,4,5], [3,4,5,6]...]...] box2: [[[4,3,2,1], [3,2,5,4], [4,3,5,6]...]...] (the number above are set arbitarily) I want to: in each batch, for each box A in box1 , find in box2 the box B which has the maximum IOU (intersection over union) with A (in the same batch, of course), and then append the tuple (A, B) to a list list_max .

问题 I dont understand the code of the gamma correction in hog.cpp in opencv, i went through some links here which doesnt match with the code in opencv hog.cpp Mat_<float> _lut(1, 256); const float* lut = &_lut(0,0); if( gammaCorrection ) for( i = 0; i < 256; i++ ) _lut(0,i) = std::sqrt((float)i); else for( i = 0; i < 256; i++ ) _lut(0,i) = (float)i; All i understood from the code is it creates 2 dimensional array of 1x256, if gamma correction is true it will calculate the square root of data.I

问题 I am done with 3d object scanning and detection with ARKit 2.0. I have scanned 3d object from all sides of object. Once 100% scanning is done then had given name to that object and then save that ARReference Object and image in document directory. Then on button click I am going to detect scanned object and display it’s name and image from document directory. Object get detected but it’s taking too much time to detect an object. I have gone through Apple document for best practices and

问题 I am currently working with the Tensorflow Object-Detection API and I want to fine-tune a pre-trained model. Therefore, a hyperparameter-tuning is required. Does the API already provide some kind of hyperparameter-tuning (like a grid search)? If there is nothing available, how can I implement a simple grid search to tune (the most relevant) hyperparameters? Furthermore, does the API provide some kind of Early Stopping function that automatically aborts the training process if the accuracy