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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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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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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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 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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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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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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Why does the AutoKeras NAS require reshaping of data?
Please take a look at the following source codes:
training.py
...
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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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Binary Classification of Images- CNN
I am learning ML and am working on a CNN problem where I need to classify images of CATS and DOGS.
The way I have setup the labels is that cats are 1 and dogs are 0. I have made the final output layer ...
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Value Error: One of the dimensions in the output is <= 0 due to downsampling in conv1d_9
i am trying to implement classification model on my dataset, which has 3 columns and 651 rows
Displacement Time Labels
0.000245879 0.01 Undamage
0.001954869 0.02 Damage
0.006545664 0.03 Undamage
0....
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Is it the right approach to select the model when it gives highest accuracy on validation dataset?
I am training the Densenet121 Model on an image dataset. I divided the dataset into 80% for training and 20% for testing.
Then I further divided the training data into 85% for training and 15% for ...
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What are the most important hyperparameters to tune to optimize the training of a 3D U-NET used for pixel classification?
Leaving aside the training loss, the optimizer (ADAM), the number of U-NET blocks (tuned to a meaningful target receptive field for the problem) and the number of filters per layer (set to the maximum ...
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Deep learning model produces very different results when classifying the same samples
I'm trying to design a simple deep learning application for biometric system verification, but every time I run the application I get very different results and I can't figure out why. I don't use ...
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Torchvision Faster-RCNN, modified loss function
I'm trying to solve a problem of table detection in spreadsheets in Excels. I've came across this paper, which suggests to use modified version of Faster RCNN to do object detection on the ...
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Looking for suggestions on the model/algorithm for 2D row labeling
As an experiment, I'd like to train a model to label a few rows of data on a 2D tensor. F.e. on a black and white image, label the "darkest" and "lightest" row.
Is this a task for ...
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How to train a CNN when the shape of the training images are too different?
I want to build a CNN model for classifying cracks. The dataset I am using has been prepared from a raw dataset. (I don't have access to that) Now the problem is, in the dataset, each image is of a ...
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Timeseries timestep changes in deployment
Im working with spatiotemporal data and I'm wondering if I train my model on a timeseries with a certain timestep, do I need to have the same timestep when I deploy the model and make predictions?
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Does the LSTM model recognize the spatial and temporal pattern in my input data?
I have data from a row of sensors that sense a body passing over it. The data is of the shape (NS, NT) where NS is the total number of sensors and NT is the total number of time steps. The sensor data ...
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3D CNN accuracy is too low, how to improve it?
I have just started learning image processing and this is my first time working on video classification. I am trying to develop a model that recognizes hand gestures using the EgoGesture dataset(more ...
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Flickr8k+PyTorch, CNN+LSTM predicts always same words during model testing
I'm a beginner in Machine Learning and I'm working with the Flickr8k dataset (it contains ~8000 images, every image has 5 captions: ~40000 pairs).
I splitted the dataset in training (70%) and ...
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What Deep Learning model to use in this spectroscopy task?
I have a task to be solved. There are energy measurements over the square area 40x40. One measurement consists of values : x, y and the energy. The all area is almost whole covered with data (a few ...
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Train CNN weights by using FFT - Reinforcement Learning?
Assume that you are doing convolution inside a CNN network, by using FFT because FFT is much way faster than using 4-5 for-loops etc.
But how should I train the weights if I know the output of my CNN ...
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Architectures for continuous vector labels prediction
I have a small dataset of feature vectors of size 200 to each corresponds a larger vector of size 1000. I would like to "predict" a large vector for every small one. The task sounds like ...
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Techniques for training a CV model based on PDF markup
Given a PDF of a home blueprint (e.g. https://www.roomsketcher.com/content/uploads/2023/04/blueprint-maker.jpg), what are techniques and approaches for training a CV model to lean how a user would ...
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Custom Loss Function in Tensorflow for UNet
I am working on a Segmentation task, where I planned to use U-Net
for the input_image of shape (224,224,3), the output should be the mask image of shape ...
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Can CNNs complete lines and contours?
Are there deep convolutional networks capable of recognizing two overlapping triangles in this image - or is this beyond the capabilities of CNNs?
And are there CNNs that can recogize two boxes ...
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How do I create an Image Dataset for a CNN?
I'm currently working on assembling a CNN for image classification with tensorflow.keras. I have all my images in a file which I already uploaded to my program. Also I have CSV-Files for training and ...
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Confusion with FC Layer Neurons and Output Shapes in CNN-based Sentiment Analysis Model
I am currently working on a sentiment analysis model for text data, and I'm using a Convolutional Neural Network (CNN) architecture. This is my first time implementing a CNN, and I'm facing issues ...
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Input shape CNN speech
The shapes of
x_train=(514, 256), y_train=(514,)
x_test=(254, 256), y_test=(254,)
I have 256 features and 770 samples of which ...
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Input dimensions for the EfficientNetV2 family of models
I have a question regarding the EfficientNetV2 family of models. If my understanding is correct there are 6 models under this family - B0 to B1 & S are the comparatively smaller models while M &...
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Gradients of lower layers of a CNN when gradient of an upper layer is 0?
Say we have a convolutional neural network with an input layer, 3 convolutional layers and an output layer.
Say the gradients with respect to the weights and biases of the third convolutional layer ...
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I have created a CNN model and now i want to draw its architecture diagram can anyone help me with that
following is my architecture:
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Issue with Convolutional Layer in Python: Getting All Zeros in Output and Terminating at a Certain Iteration
I'm currently working on implementing a convolutional layer in Python for a natural language processing model. However, I've encountered an issue with the convolutional layer that I can't seem to ...
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Binary classification metrics for one-hot label encoding in Tensorflow
I run a binary classification using different CNN versions in Tensorflow.
When I label samples from each class using 0 and 1, I select a sigmoid output in the last layer of the CNN, like
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Dropout and BatchNorm decrease speed of learning
Experimenting with the cifar10 dataset and faced with strange behavior when Dropout and BatchNorm don't help at all.
As I get:
Dropout - freezing some of the weights which helps us to prevent ...
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Why doesn't loss decrease with each epoch (for IMDB data vs Rotten Tomatoes data)
I am following the Google tutorial on ML for text classification
I made this Google Colab notebook which you should be able to run from start to finish to see the issue.
When the code trains a ...
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Object detection on largest number of classes
Does anyone know any pretrained object detection models to run with python with highest number of objects to be recognised? Yolo finds 80 objects, it is good if I can find a larger number. It would be ...
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Dimension of the output of a convolution
In the image you can see the input and output dimensions in the second layer of a CNN. If the input of the convolution is 32 arrays of size 14x14 and we apply 64 kernels to it, shouldn't we get as ...
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Implementation of Graph Neural Network for Image Classification
I'm currently working on a project where I want to utilize Graph Neural Networks (GNNs) for image classification tasks. However, I'm facing difficulties in understanding how to implement GNNs ...
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Better results when adding a dropout layer before a single layer classifier - counter intuitive result
I am working on an multi-class image classification problem (with 9 classes), i am using a pretrained DenseNet121 (on ImageNet), i'm using Keras.
i am using densenet as a feature extractor, with a ...
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Tips regarding using a CNN to teach a model car (JetRacer) to park
I'm using a model car which can be remotely controlled and which has a front facing camera.
My goal is to train a CNN which would be able to efficiently park this car in a simple environment (pictured ...
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Why apply min-max normalization to each individual mel spectrogram for a training set?
I am watching a tutorial on using mel spectrograms to classify the audio's genre via CNN. My question is why apply local min-max normalization to each individual mel spectrogram? What I mean by local ...
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How to backpropagate transposed convolution?
I'm currently learning Convolutional Neural Networks and am stuck on trying to figure out how to compute gradients in a layer that uses transposed convolution. Also, how do I calculate the gradients ...
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Where exactly in YOLO's architecture is the input image divided into a grid?
I am currently studying the YOLO algorithm for a project. What I'm not quite sure about is where exactly the input image is divided in an SxS grid. After my research on the paper, videos and websites ...
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Best practice labeling grouped anomalies for object detection
I would like to train object detection model (e.g. YOLO) for images that contain anomalies. The anomalies are essentially the holes in a surface of different sizes. How do I label correctly such ...
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Validation Loss not decreasing for RESNet model
I have been trying to train a Resnet model to classify Diabetic Retinopathy images into binary classes. The dataset consists of around 35k images. The val loss and accuracy does seem to behave weirdly ...
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How to correctly measure the inference time and FLOPs of a model?
For some reason, I can’t find built-in solutions (not really?) in keras and tensorflow, while on the site
https://keras.io/api/applications/ they provide Time (ms) per inference step (CPU), but for ...
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How to implement CNN crop area as learnable parameter?
I'm currently implementing a 1D CNN to forecast a time series for an industrial process. Essentially, I give the model 30 time steps (1 time step = 1 minute) of input data captured from 7 different ...
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