image-classifier | triplet loss , batch triplet loss | Machine Learning library

 by   gustavkkk Python Version: Current License: MIT

kandi X-RAY | image-classifier Summary

kandi X-RAY | image-classifier Summary

image-classifier is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. image-classifier has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However image-classifier build file is not available. You can download it from GitHub.

python, triplet loss, batch triplet loss, kaggle, image classifier, svm
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              image-classifier has a low active ecosystem.
              It has 8 star(s) with 4 fork(s). There are 1 watchers for this library.
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              It had no major release in the last 6 months.
              image-classifier has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of image-classifier is current.

            kandi-Quality Quality

              image-classifier has no bugs reported.

            kandi-Security Security

              image-classifier has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              image-classifier is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

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              image-classifier releases are not available. You will need to build from source code and install.
              image-classifier has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.

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            image-classifier Key Features

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            image-classifier Examples and Code Snippets

            No Code Snippets are available at this moment for image-classifier.

            Community Discussions

            QUESTION

            What is the use of validation data?
            Asked 2019-Dec-11 at 18:28

            I am following this tutorial to build a classifier: https://towardsdatascience.com/a-simple-cnn-multi-image-classifier-31c463324fa In this part of the code:

            ...

            ANSWER

            Answered 2019-Dec-11 at 18:24

            The idea is to avoid overfitting, or in other words learning more about specific examples than general characteristics. If you just tested your model on the training data, it could easily just learn the (lets say) 1000 cat pics you wanted to distinguish from the 100 dog pics, by 'memorizing' those 100 pics - a CNN could easily have an amount of memory in its weights equivalent to 100 pics. And obviously its not the entire pic that needs to be memorized but only something that distinguishes those particular cat pics from those particular dog pics. This can happen anytime the number of free params in the model can compete with the amount of information in the training set. To avoid this the test should be on another set of data, the validation set. But the same thing can happen with the validation set ! If the network is set to minimize error on the validation set that's what it will do, and thus the validation set may itself become overfit. So a third test is used (in principle only once, to avoid yet again overfitting on this data, and so on ) for final evaluation.

            Source https://stackoverflow.com/questions/59291741

            QUESTION

            Error: Tensorflow Op is not supported: AddN
            Asked 2019-Sep-20 at 16:51

            I've retrained a Mobilenet V1 model using my own dataset. Right now, I'm trying to get my model to load into this example project: https://github.com/shivangidas/image-classifier

            It keeps throwing the following error

            ...

            ANSWER

            Answered 2019-Sep-20 at 16:51

            The addN operation is supported by the converter according to this list. It looks like you are using a rather old version of Tensorflow.js. I noticed the function loadFrozenModel which has been renamed to loadGraphModel since version 1.0 (released in March 2019).

            Converting addN is supported since version 0.5.6 (see this commit). If your Tensorflow.js version is older than 0.5.6 you can simply upgrade to a more recent version and it should work.

            Source https://stackoverflow.com/questions/58024781

            QUESTION

            Cannot run Flask on Docker (ModuleNotFoundError)
            Asked 2019-Aug-05 at 12:40

            I have been trying to run my Python API (using Flask) with Docker for a while, but keep running into this issue:

            ModuleNotFoundError: No module named 'flask'

            Running this on Mac OS X (10.14.5) with Docker version 19.03.1, build 74b1e89.

            My Dockerfile looks like this:

            ...

            ANSWER

            Answered 2019-Aug-05 at 12:11

            you need just to specify the correct Python version, so you need just to change your entrypoint to:

            Source https://stackoverflow.com/questions/57357154

            QUESTION

            Following a TensorFlow tutorial and am not getting accurate model predictions
            Asked 2019-Jul-05 at 03:09

            I am following a tutorial on TensorFlow image classifications.

            My use case differs slightly from the tutorial, it uses Chess pieces, whereas I am using traffic lights, and want to detect if its red, green or amber.

            I am finding that the results of my tests are poor,and wonder if it is to do with the cv2.IMREAD_GRAYSCALE I see in the CreateData section of the tutorial. Of course colour matters in my classifier, so I wonder if the tutorial is converting to greyscale, hence my lack of accurate results.

            I therefore changed all references of cv2.IMREAD_GRAYSCALE to cv2.IMREAD_COLOR, reran the CreateData routines, then tried to run the NeuralNetwork creation program, but that then fails with error:

            ...

            ANSWER

            Answered 2019-Jul-05 at 03:09

            Clearly, the error shows that it is treating each channel of a color image as a separate greyscale image. That's why "Found 195 input samples and 65 target samples" i.e 3 times more. So, you should look in the code where data is prepared. Check line 53

            Source https://stackoverflow.com/questions/56895339

            QUESTION

            Following a TensorFlow tutorial and hitting issues with model.predict
            Asked 2019-Jul-05 at 00:21

            I am following a tutorial for TensorFlow and I am having problems during the model prediction phase.

            The final bit of code is :

            ...

            ANSWER

            Answered 2019-Jul-05 at 00:21

            Your image should be prepare_file(img_path) instead of just a string.

            Source https://stackoverflow.com/questions/56895221

            QUESTION

            Tensorflow AttributeError: 'NoneType' object has no attribute 'keys'
            Asked 2019-Jun-13 at 04:09

            I am trying to make a simple Image Classifier using Tensorflow. From here https://medium.com/@linjunghsuan/create-a-simple-image-classifier-using-tensorflow-a7061635984a

            I am using Anaconda2 on Windows 10 (64bit) Packages used The following NEW packages will be INSTALLED:

            ...

            ANSWER

            Answered 2018-Mar-29 at 01:25

            You are not supplying all the required parameters in the command line

            Windows example from the post you linked: example (SO won't let me post images. )

            If the file is stored in C:\training_data and assuming your working directory is F:\Tensorflow then the command is

            python F:\Tensorflow\retrain.py --image_dir C:\training_data --how_many_training_steps 500 --model_dir F:\Tensorflow\inception --output_graph=F:\Tensorflow\retrained_graph.pb --output_labels=F:\Tensorflow\retrained_labels.txt

            Source https://stackoverflow.com/questions/49545926

            QUESTION

            IotEdge - Error calling Create module image-classifier-service
            Asked 2019-May-28 at 16:59

            I'm very new to Azure IoT Edge and I'm trying to deploy to my Raspberry PI : Image Recognition with Azure IoT Edge and Cognitive Services but after Build & Push IoT Edge Solution and Deploy it to Single Device ID I see none of those 2 modules listed in Docker PS -a & Iotedge list And when try to check it on EdgeAgent Logs there's error message and it seems EdgeAgent get error while creating those Modules (camera-capture and image-classifier-service)

            I've tried : 1. Re-build it from fresh folder package 2. Pull the image manually from Azure Portal and run the image manually by script

            I'm stuck on this for days.

            in deployment.arm32v7.json for those modules I define the Image with registered registry url :

            ...

            ANSWER

            Answered 2019-May-28 at 16:59

            When you pulled the image directly with docker run, it pulled but then failed to run outside of the edge runtime, which is expected. But when the edge agent tried to pull it, it failed because it was not authorized. No credentials were supplied to the runtime, so it attempted to access the registry anonymously.

            Make sure that you add your container registry credentials to the deployment so that edge runtime can pull images. The deployment should contain something like the following in the runtime settings:

            Source https://stackoverflow.com/questions/56290353

            QUESTION

            How to "convert" this unix command line prompt for using in Windows cmd?
            Asked 2019-Apr-09 at 13:27

            enter image description hereso I try to do this macOS tutorial to build an image classifier with tensorflow on a Windows PC, therefore the following prompt doesn't work in the Windows cmd:

            ...

            ANSWER

            Answered 2019-Apr-09 at 12:56

            QUESTION

            Why does Keras tell me "ValueError: setting an array element with a sequence." despite having all arrays as numpy arrays?
            Asked 2019-Mar-31 at 09:14

            I am trying to train a 2D neural network using keras. I have a weird error message, "ValueError: setting an array element with a sequence." when I try to use model.fit function in keras. Specifically, the error says that my "tensor_train_labels" is a sequence instead of an array. But my labels are indeed numpy arrays (not a sequence). I am not sure why does keras complain about it ?

            I am following this tutorial for building my network

            ...

            ANSWER

            Answered 2019-Mar-31 at 09:14

            The possible error is that you have arrays of different sizes when you are trying to convert it into the numpy array. Possible solution : https://stackoverflow.com/a/49617425/8185479

            Source https://stackoverflow.com/questions/55439302

            QUESTION

            How to feed an image ROI into session.run() of Tensorflow?
            Asked 2019-Mar-12 at 12:19

            I am trying to feed my image roi into the Tensorflow classifier I took from here. The idea is to first run a simple filter, get rectangle candidates, and then check (using the network) whether each rectangle(roi) is actually what I am looking for.

            ...

            ANSWER

            Answered 2019-Mar-08 at 15:12

            feed_dict expect a dictionary with tensors as keys, to populate the placeholders with the specified valued. It's not in your code snippet how does the screw_id is initiated, but I bet it's not a tensor of any kind, hence, your error.

            Source https://stackoverflow.com/questions/55065471

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