cifar-10 | Simple C++ reader for CIFAR-10 dataset | Dataset library

 by   wichtounet C++ Version: Current License: MIT

kandi X-RAY | cifar-10 Summary

kandi X-RAY | cifar-10 Summary

cifar-10 is a C++ library typically used in Artificial Intelligence, Dataset applications. cifar-10 has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Simple C++ reader for CIFAR-10 dataset
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              cifar-10 has a low active ecosystem.
              It has 15 star(s) with 6 fork(s). There are 2 watchers for this library.
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              It had no major release in the last 6 months.
              There are 1 open issues and 0 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of cifar-10 is current.

            kandi-Quality Quality

              cifar-10 has no bugs reported.

            kandi-Security Security

              cifar-10 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              cifar-10 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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              cifar-10 releases are not available. You will need to build from source code and install.

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            cifar-10 Key Features

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            cifar-10 Examples and Code Snippets

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            Community Discussions

            QUESTION

            Using Cifar-10 dataset from tfds.load() correctly
            Asked 2021-May-21 at 11:48

            I'm trying to use the Cifar-10 dataset to practice my CNN skills.

            If I do this it's ok:

            ...

            ANSWER

            Answered 2021-May-21 at 11:44

            You can also extract them like this:

            Source https://stackoverflow.com/questions/67635660

            QUESTION

            Get corresponding class id given class name from a dataset
            Asked 2021-May-21 at 10:43

            I'm trying to work with the Cifar-10 dataset using Tensorflow as this,

            ...

            ANSWER

            Answered 2021-May-21 at 10:00

            QUESTION

            PyTorch Boolean - Stop Backpropagation?
            Asked 2021-May-13 at 21:22

            I need to create a Neural Network where I use binary gates to zero-out certain tensors, which are the output of disabled circuits.

            To improve runtime speed, I was looking forward to use torch.bool binary gates to stop backpropagation along disabled circuits in the network. However, I created a small experiment using the official PyTorch example for the CIFAR-10 dataset, and the runtime speed is exactly the same for any values for gate_A and gate_B: (this means that the idea is not working)

            ...

            ANSWER

            Answered 2021-May-13 at 18:28

            You could use torch.no_grad (the code below can probably be made more concise):

            Source https://stackoverflow.com/questions/67523716

            QUESTION

            Python TypeError: 'Tensor' object is not callable when sorting dictionary
            Asked 2021-Apr-09 at 06:50

            Here is my code. The packages imported are not shown. I am trying to feed the CIFAR-10 test data into alexnet. The dictionary at the end needs to be sorted so I can find the most common classification. Please help, I have tried everything!

            ............................................................................................................................................................................................................................................................................................................

            ...

            ANSWER

            Answered 2021-Apr-09 at 06:50

            This line is the problem

            Source https://stackoverflow.com/questions/67011955

            QUESTION

            Understanding input and output size for Conv2d
            Asked 2021-Mar-29 at 12:38

            I'm learning image classification using PyTorch (using CIFAR-10 dataset) following this link.

            I'm trying to understand the input & output parameters for the given Conv2d code:

            ...

            ANSWER

            Answered 2021-Mar-29 at 07:36

            These are the dimensions of the image size itself (i.e. Height x Width).

            Unpadded convolutions

            Unless you pad your image with zeros, a convolutional filter will shrink the size of your output image by filter_size - 1 across the height and width:

            3-filter takes a 5x5 image to a (5-(3-1) x 5-(3-1)) image Zero padding preserves image dimensions

            You can add padding in Pytorch by setting Conv2d(padding=...).

            Chain of transformations

            Since it has gone through:

            Layer Shape Transformation one conv layer (without padding) (h, w) -> (h-4, w-4) a MaxPool -> ((h-4)//2, (w-4)//2) another conv layer (without padding) -> ((h-8)//2, (w-8)//2) another MaxPool -> ((h-8)//4, (w-8)//4) a Flatten -> ((h-8)//4 * (w-8)//4)

            We go from the original image size of (32,32) to (28,28) to (14,14) to (10,10) to (5,5) to (5x5).

            To visualise this you can use the torchsummary package:

            Source https://stackoverflow.com/questions/66849867

            QUESTION

            ValueError: TensorFlow2 Input 0 is incompatible with layer model
            Asked 2021-Mar-05 at 19:48

            I am trying to code a ResNet CNN architecture based on the paper by using Python3, TensorFlow2 and CIFAR-10 dataset. You can access the Jupyter notebook here.

            During training the model using "model.fit()", after just one epoch of training, I get the following error:

            ValueError: Input 0 is incompatible with layer model: expected shape=(None, 32, 32, 3), found shape=(32, 32, 3)

            The training images are batched using batch_size = 128, hence the training loop gives the following 4-d tensor which TF Conv2D expects- (128, 32, 32, 3).

            What's the source of this error?

            ...

            ANSWER

            Answered 2021-Mar-05 at 19:48

            Ok, I found a small issue in your code. The problem occurs in the test data set. You forget to transform it properly. So currently you have like this

            Source https://stackoverflow.com/questions/66496888

            QUESTION

            Object detection very low accuracy
            Asked 2021-Jan-21 at 08:52
            • I am trying to make cifar-10 object detection. However, all I can get is %20 accuracy..
            • I don't use any preprocessing though but still isn't %20 is too low? Also I use Canny Edge Detector and saved those edges to some folder.
            • When I use that edge images of Canny output instead of Gray Scale normal images, accuracy gets drop down to %15...
            • I believe while using the output of the Canny I should get a higher accuracy, not lower. Here is how I create data generator:
            ...

            ANSWER

            Answered 2021-Jan-21 at 08:38

            CIFAR-10 is a difficult dataset to generalize with 90%+ accuracy. You can try:

            • More complex network
            • Flattening 1 time ( I see you did it two times, that might affect.)
            • Using more augmentation options like zooming etc.
            • Using callbacks such as ReduceLROnPlateau
            • While making the network complex, you should use regularization to avoid overfitting.

            After flattening you can use Dense layers like this, you didn't pass any activations to your dense layers at the end:

            Source https://stackoverflow.com/questions/65822423

            QUESTION

            Getting a very low accuracy on implementing Neural Network in Keras
            Asked 2021-Jan-18 at 15:49

            I am trying to implement ANN on a Cifar-10 dataset using keras but for some reason I dont know I am getting only 10% accuracy ?

            I have used 5 hidden layers iwth 8,16,32,64,128 neurons respectively.

            This is the link to the jupyter notebook

            ...

            ANSWER

            Answered 2021-Jan-18 at 15:49

            That's very normal accuracy for a such network like this. You only have Dense layers which is not sufficient for this dataset. Cifar-10 is an image dataset, so:

            • Consider using CNNs

            • Use 'relu' activation instead of sigmoid.

            • Try to use image augmentation

            • To avoid overfitting do not forget to regularize your model.

            Also batch size of 500 is high. Consider using 32 - 64 - 128.

            Source https://stackoverflow.com/questions/65777704

            QUESTION

            Python save images with open CV to an array in a sorted way according to the image name
            Asked 2021-Jan-15 at 13:24

            I have 50.000 in a folder that have names "1.png" to "50000.png". And I want to save them to some array in spesicfic order, because I have labels for them in order to classify them. So when order of the images get messed up, labels become useless. (i.e: 1.png will be saved to first index and 2.png will be saved to second index etc.). I try this code for this purpose:

            ...

            ANSWER

            Answered 2021-Jan-15 at 13:24

            It is in order though.

            Since image_folder is a list of strings, image_folder.sort() will perform lexicographical sorting, where "1" < "10" < "100" < "1000" < "1001" < ... < "2" < "20" and so on.

            If you want to treat "100.png" like "integer 100 + file extension", do this:

            Source https://stackoverflow.com/questions/65736236

            QUESTION

            PyTorch convolutional block - CIFAR10 - RuntimeError
            Asked 2021-Jan-08 at 10:07

            I am using PyTorch 1.7 and Python 3.8 with CIFAR-10 dataset. I am trying to create a block with: conv -> conv -> pool -> fc. Fully connected layer (fc) has 256 neurons. The code for this is as follows:

            ...

            ANSWER

            Answered 2021-Jan-08 at 07:38

            you need to flat the output of nn.MaxPool2d layer for giving input in nn.Linear layer.

            try to use x = x.view(x.size(0), -1) before giving input to fc layer for flatten tensor.

            Source https://stackoverflow.com/questions/65624700

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

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