Questions tagged [cnn]
Convolutional Neural Networks (CNN, also called ConvNets) are a tool used for classification tasks and image recognition. The name giving first step is the extraction of features from the input data.
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Conv-2 CNN architecture - CIFAR-10
I have a CNN architecture for CIFAR-10 dataset which is as follows:
Convolutions: 64, 64, pool
Fully Connected Layers: 256, 256, 10
Batch size: 60
Optimizer: ...
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How can I tell if my CNN tuning made a difference?
I'm working on a detection CNN, estimates pose for some classes of objects. I am able to compute a bunch of different metrics on performance, things like position error, rotation error, tracking ...
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Is it ok to use photo collages as dataset instead of single images for training the object detection ssd model?
Is it normal/better to use photo collages (multiple photos in one image) as a dataset instead of single images for training the object detection SSD model?
I am using Tensorflow Object Detection API ...
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Training with few samples, dropping training loss but constant validation loss
I am training a resnet50-based model using transfer learning. My dataset has 10 classes and about 10 occurrences per class, so it is very small. The training loss is decreasing steadily to 0.07 for ...
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My validation loss is too much higher than the training loss is that overfitting?
I am new to data deep learning. I am educating myself but I don't understand this situation. Where Validation loss is much much higher than the training loss. Can someone please interpret this?
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CNN vs RNN: Classification on 20_newsgroup data
I am very much a beginner in ML space. I am learning keras to get hands-on experience.
I picked the classification of 20_newsgroups data for my task, I used glove.6B.50d.txt for embeddings.
I chose ...
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Design a CNN that can detect the face in the given input image
a. What strategy will you use? How many layers? How many filters?
b. Describe the filters for each layer in detail.
c. Show how the convolutions will work and demonstrate that your CNN will be able to ...
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What may cause the CNN layer weight regularizer to reduce the model accuracy
What may cause the accuracy reduction when using the tf.keras regularizer at layers in CNN in the symptom? The example is simple but it happens with more complex CNN causing no improvement during the ...
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How do I get my Neural network to ignore certain values?
I was wondering if there was a way that I can get my CNN encoder-decoder neural network to completely ignore certain values in my data (2d images).
There are some pixel values of 0 that never change ...
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Why is convnet transfer learning taking so long?
I am using transfer learning to train a binary image classification model using keras' pretrained VGG16 model. The code can be found below :
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how do I label my images for computer vision
So I want to do shape recognition task on a flowchart using CNN, but my input images are not labeled and I don't know how to do that automaticaly I mean not manually, anyone can help me please ?
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best approach for CNN training with multiple subcategories and one category
I need to classify pictures into 2 categories: approved and rejected.
Rejected category has different type of images which are not allowed (subcategories), for example nude or gore or anime etc.
What ...
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How do I find output size of a network?
The input to a convolutional layer of a neural network is an image of size $128\times 128\times 3.$
$ 40$ convolutional filters of size $5\times 5$ are applied to it. Would you get an output? If no ...
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Why does the computation time drastically increase when increasing kernel size in a CNN?
I'm doing some experimentation with a CNN and have 2 conv layers with 32 and 64 filters respectively. I started out with 3x3 kernel sizes and noticed that when I increased it to 5x5, 7x7 etc the time ...
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What are some general tips to improve my MNIST classifier?
I have built a CNN from scratch in python using Numpy, to tackle the MNIST hand-written digit recognition problem. It's composed out of a convolutional layer (3 3x3 filters), a maxpooling layer (2x2 ...
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Preventing fitting Regression CNN to the mean when dataset has only few outliers
I am trying to train a CNN for regression on a dataset where most of the points lie around a similar output value. There are however a few outliers that are very important but they are less ...
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ValueError: Layer model expects 2 input(s), but it received 3 input tensors using generator
I am trying to fit a model using generator function and I get the following error:
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Is 500 epochs too much for a CNN project?
I am working on a project where I need to train a model with a data set of 250 images. My epochs count is 500. Is that too much? Will it overfit?
I did this because ...
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Can we use both validation loss and cross validation at the on CNN?
I have seen that validation loss is used for avoiding overfitting of the training set and cross-validation is used for generalized the models' results.
Are they use for similar purposes or results? ...
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How to deal with severe overfitting in a UNet Encoder/Decoder CNN in a task very similar to image translation?
I am trying to fit a UNet CNN to a task very similar to image to image translation. The input to the network is a binary matrix of size (64,256) and the output is of size (64,32). The columns ...
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Preprocessing for finetuned CNN model from pretrained models
Is it necessary to preprocess the images the same way as they were during the training of pretrained models in our finetuned model to use it for a different classification task ? Say, I have a ...
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Converting a Standard LSTM RNN over to a Transformer Model
I am looking for some advice on converting my existing CNN/LSTM RNN over to a Transformer type model. This regression model takes a sliding window size of 240 rows with 33 features. It aims to ...
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Why does my CNN validation loss increase immediately, even with lots of data?
The Issue
I've been working on a regression CNN implementation to predict time series data and have run into an issue where my validation loss and training loss diverge immediately during training, as ...
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How to fetch text from pdf to further proceed with question answer based model from the same document?
To illustrate the above title.
Suppose you have a pdf document, which is basically scanned from hardcopy, now there are set of fixed questions to answer from the document itself.
For an example a ...
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How does ResNet bottleneck architecture's input size is possible to change from 56x56x64 to 56x56x356?
In ResNet papaer, First residual block's input size is 56x56x64 caused by 7x7x64 filter in first layer. But, in the paper, they showed residual block that has 56x56x256 input size. How does it is ...
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Adding multi-image context to a CNN
I'm looking for an approach to classify a similar dataset to the exposed next. Let's say we have an image with some elements inside it (imagine a large building footprint with several structures). ...
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What is a sliding-window convolutional neural network?
In the abstract of "U-Net: Convolutional Networks for Biomedical
Image Segmentation", the authors mention a sliding-window convolutional neural network. I've found several other articles ...
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Does it make sense to interpolate image just before a CNN?
I'm training a CNN with images which have lots of horizontal black lines (due to the nature of the sensor). I'm thinking in removing this artifacts by some kind of preprocessing (interpolation, median ...
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Semantic segmentation with greyscale images
I'm trying to reproduce a research with greyscale images instead of colour images.
I have found that there are pre-trained networks, like VGG16, with ImageNet. But that dataset has colour images, and ...
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Can the same CNN architecture be used for different data sets?
I have a CNN architecture that works well on 32x32x3 images. Can I use that same architecture for a data set made up of 28x28x1 images? (Both data sets have 10 classes). If this is possible, what ...
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Why does the AutoKeras NAS require reshaping of data?
Please take a look at the following source codes:
training.py
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Deep learning test loss curve won't go down
I've been working with Deep Learning projects for this current project that I am working on and it's basically a time series classification problem. Where given an array of time series data I need to ...
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AttributeError: 'Functional' object has no attribute 'predict_classes''
I am trying to use run a GoogLeNet code using FERET datasets. When I run the code, I get the following error message:
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Detecting features in XY Plots using CNNs
I have a simple classification problem - if two features plotted on a simple XY plot show a "kink" or characteristic turn, then the label is TRUE, otherwise FALSE. I've been attempting to detect the "...
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What doese 'v' mean in GoogLeNet?
In GoogLeNet (this link), there is 'v' notation in Figure3 like '1X1+1(v)'.
I don't know the meaning of 'v'.
Also, I understood 's' as stride. But, I don't know the reason why plus operation is used ...
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after overcoming the overfitting, how to increase training accuracy?
I am building a CNN using keras for a classification task. I started with a simple model as a starting point and as almost all ML problems go, especially if the dataset is not very big, I faced an ...
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Training the document page layout and classifying good/bad layouts
I have a use case where I am supposed to get the coordinates of each block element in a page (whether its paragraph, image, table) where I train a model to understand how they are placed in a given ...
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CNN implementation low accuracy on MINST data
I'm trying to implement VGG11 (Model A of Table 1 from this article) on the MINST dataset but I'm getting ~10% train & test accuracy (as bad as random guessing). I had to resize the MINST data ...
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CNN for subsets of a dataset - how to tune hyperparameters
I have a dataset and would like to train CNNs on subsets of different size of the dataset.
I already have a CNN, which classifies very well if I use the entire dataset.
Now the question arises if I ...
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How to put data into a 1-dimensional ConvLSTM2D with keras?
I am attempting to adapt the frame prediction model from the keras examples to work with a set of 1-d sensors. I have android wearable sensor data and am designing an algorithm that can hopefully ...
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False positive in Multi class Image classification
I am training a neural network with some convolution layers for multi class image classification. I am using keras to build and train the model. I am using 1600 images for all categories for training. ...
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How to determine the number of the training images in Keras after data augmentaion?
I want to create a CNN model and I am using data augmentation. I want know the number of augmented images in Keras.
How to determine the number of the training images in Keras after data augmentation?...
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Reducing Validation loss for Triplet Loss Embeddings
I'm trying to create a facial recognition detector using triplet loss followed by a kNN algorithm.
I have roughly 10000 input images with 3 different classes, input size is 80x80. Model structure uses ...
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Why are my predictions broken when performing image segmentation with TensorFlow?
I am attempting semantic image segmentation with TensorFlow. Just to get something working, I am taking this one training image, training the network on that image for a little while, and then "...
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Division of numbers from contours in opencv in python for cnn
I want to separate numbers in suppose 7638 into different images which can be predicted individually using cnn.
By finding contours how can I divide each contour into separate image in python.
To be ...
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Classification problem in pytorch with loss function CrossEntropyLoss returns negative output in prediction
I am trying to train and predict SVHN dataset (VGG architecture). I get very high validate/test accuracy by just getting the largest output class. However, the output weights are of large positive and ...
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CNN model low accuracy
I have 1299 images in 4 classes (374/269/284/372). I want to use the VGG19 model, add a dense layer at the top and fine-tune it with my images. As I only have 1299 images, I also want to use data ...
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How to use CNN to deal with a 2D regression problem?
I have seven measurements (Obs1-7), each measurement has the dimension of [x,y,t] where x and y are coordinates and t is time. Now I want to build a model that uses the first 6 measurements to predict ...
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How long does it typically take to train a MNIST data on a Mac Pro?
My code is below:
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Tensorflow outputs nan for basic object detection/classification
I am receiving nan as my accuracy and loss outputs after each epoch for basic object detection in tensorflow. Also, my results (classification and bounding box ...
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