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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 to include context / additional information in a NN

Newbie here. This is a generic question. Let's imagine I'm trying to identify fish from phone pictures, using CNN. In some cases, there are fish species/group that look similar, but one is exclusively ...
terauser's user avatar
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Multi-step CNN-LSTM Encoder Decoder Model is not fitting well on peak values

I am trying to predict 4 values concurrently for next 24 hours n_lookback = 48 n_forecast = 24 I am breaking the sequences like this: ...
Hiba Rehman's user avatar
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Is my SPP layer well implemented on the CNN model?

Im doing a CNN model with transfer learning from a VGG16 model but Im adding a Spatial Pyramid Pooling layer on top, I have tried with different data-bases and it has worked, but I'm not sure if its ...
Belat's user avatar
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TCN vs CNN models

I can't understand the difference between the two architectures. The TCN should consist in 1D convolutions over layers of neurons, and in this way it gather some info from a time series. The biggest ...
AlteD1995's user avatar
1 vote
0 answers
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Transfer Learning - GoogLeNet - Training Times || Loss not converging || Pytorch

Hi Community and thanks in advance for the help. I am working on transfer learning - specifically GoogLeNet model with the Food101 Dataset. Code is below. I think everything is in order from data ...
James's user avatar
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1 answer
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How will weights learn in CNN for multi class classification?

How are the weights of filters in a CNN can learn meaningful features in multiclasses classification if they keep changing as different images are passed through the network during training.Say we are ...
Jai's user avatar
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0 answers
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Create model for manufacturing defect detection using python

Has anyone done some work on manufacturer defect detection? I need some input on this. I have done some research and found some examples like TensorFlow/Keras and CNN, but I need some real-time ...
Reetesh Nigam's user avatar
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Losing Information while resizing the image in Segmentation task using U-net

I'm using U-net architecture to build a segmentation task of image. During training I have image of size 256256 image. It works very well on the segmentation of same size 256256 or near to size 256*...
Akshit Dhillon's user avatar
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Are my CNN loss and performance curves valid, or are they showing under or overfitting?

Thanks in advance for any help offered. I am using a Keras CNN to perform binary classification (credit card transactions fraud vs non-fraud). Below is my results for 100 epochs. It feels odd that the ...
luckylogic's user avatar
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0 answers
21 views

How to improve Accuracy on dermaMNIST dataset?

Unlike the regular MNIST which gets 97-99% with a fairly basic network, dermaMNIST gets training/validation stuck on 0.69. This tells me the model is underfitting. But, making it bigger seems to have ...
Zwerchhau's user avatar
0 votes
1 answer
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How do I ensure final output shape matches input shape for a semantic segmentation task?

I trying to replicate the semantic segmentation example https://keras.io/examples/vision/oxford_pets_image_segmentation/ but train on my own data. I have 8 labels (7 features + background). My images ...
utx7563yu's user avatar
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1 answer
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Applying dropout effectively in CNN

I am fairly new to deep learning and machine learning in general and have been trying to teach myself. I’m interested in understanding when and how to effectively use dropout in a CNN. While ...
Nile's user avatar
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0 answers
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Adding sliding window dimension to data causes error: "Expected 3D or 4D (batch mode) tensor ..."

I wrote a pytorch data loader which used to return data of shape (4,1,192,320) representing the 4 samples of single channel image, each of size ...
Mahesha999's user avatar
1 vote
1 answer
42 views

How to comment on goodness of loss functions?

I have two loss functions $\mathcal{L}_1$ and $\mathcal{L}_2$ to train my model. The model is predominantly a classification model. Both $\mathcal{L}_1$ and $\mathcal{L}_2$ takes are two variants of ...
Aleph's user avatar
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Trained model on cifar10 performs poorly on real images

So I'm trying to train a model using the CIFAR10 dataset. The problem is that while the performance of the model on validation and test sets are good (about 95-96%), the model fails to predict images ...
AlbertDang's user avatar
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24 views

how to calculate the dimension of the output in this cnn?

I have been reading a book about CNNs and they have put the following: Here it makes sense the part that explains how each feature map is formed with the inputs convoluted by the kernels. The problem ...
Lila's user avatar
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i need to improve accuracy of following code. it have 1 dataset folder having 7 folders. there are total 3076 images

importing libraries ...
raman deep's user avatar
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Why is my 3D CNN stuck at constant accuracy?

I am writing a CNN for binary classification MedMNIST data: https://medmnist.com/, specifically the Lung Nodule 3D dataset (N=1633 and 7:1:2). Currently, my model is not training at all; it is either ...
Matthew H's user avatar
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9 views

Character-wise accuracy for image-to-text models

is it possible to enforce image-to-text models like ViT or a simple CNN+Transformer to achieve character-wise accuracy? Here's the context of my project: I am developing a model to extract some ...
CarlV's user avatar
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1 vote
0 answers
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How to apply this schematic of a CNN?

I'm trying to apply a model from a paper to my problem, however I get very poor results (R² = 0.1 for regression). I think I don't understand the schematic drawing of the CNN used in the paper. Could ...
YPOC's user avatar
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IoU metric for multi class image segmentation task

My input shape is of (168,18). I create batches of size 256 and create my dataset using timeseries_from_Array_dataset. I am visualizing this 2D snapshot of a multivariate timeseries (batch size- 256, ...
Vjs's user avatar
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Help with a MaNet finetuning (binary semantic segmentation task)

Introduction: I am currently working on a computer vision problem, I have satellite images and I have to detect a particular archeological structure (Tell). I have access to the previously made ...
Alessandro Pistola's user avatar
1 vote
2 answers
31 views

Image segmentations vs image detection

If I need to detect on an image some objects and we are only interested in counting them, between image segmentation and object detection which one would you think would yield best results in terms of ...
Dinu Mihai's user avatar
1 vote
2 answers
32 views

Using a Genetic Algorithm in junction with a digit classifier CNN to create an "MNIST image generator"

I'm trying to use a genetic algorithm that optimizes a 28x28 matrix (its shape) to make it look like an image of the number 7 that could be found in the MNIST image dataset. My attempt is to basically ...
kal_elk122's user avatar
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Is it fair to say that Hausdorff Distance (HD) focuses on low level details while dice score (DSC) high level

I wonder if its make sense to say that Hausdorff Distance (HD) measures low-level details while dice score (DSC) focuses on high levels. If you could cite a paper, I would appreciate it.
user836026's user avatar
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CNN segmentation models: class weights specification on IoU metric

I am building a MANet model using pytorch lightning. For getting the model I use the library segmentation models. As my objective is to do binary semantic segmentation, during the test phase I ...
Alessandro Pistola's user avatar
1 vote
1 answer
26 views

Building a CNN (with Keras for pixelwise classification)

I have a set of 120x120 input images with 3 channels. I want to build a basic CNN to predict the value of each pixel. I have 2 doubts. One is regarding the last layer - should be a Dense layer, or a ...
Filippo Nunes's user avatar
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0 answers
9 views

What I do wrong with my speech recognition CTC model

I want to train an english speech to text model using architecture similar to deepspeech. In general it has 4 blocks: feature extraction I used melspectrogram. (I used n_mels=80) This translates (...
Захар Наумець's user avatar
1 vote
1 answer
52 views

How does ReLU function make it possible to let the CNN learn more complex features in input data?

In many descriptions of a CNN i often read that at the end of the Convolutional layer, a ReLU function is needed, for two reasons: first it solves many problems about the vanishing gradient problem, ...
J. Drawman's user avatar
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20 views

How to think when designing deep learning architectures?

My question is about people studying deep learning. There is a point that bothers me and prevents me from working and understanding the articles. Especially in articles containing CNN-based deep ...
ylcnkmetu's user avatar
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How should I design the CNN for classifying the relation between 2 texts (multiple classes)

So I have a task to classify the relation between 2 texts (4 classes possible) and one of the requirements is to preprocess them with TfidfVectorizer or CountVectorizer. Since every sample has 2 ...
giza2001s's user avatar
2 votes
1 answer
53 views

What does it mean if a neural networks starts overfitting more after applying regularisation techniques

Background I am building a CNN to categorize cytometric cell data into healthy and diseased groups. The architecture looks as follows: 3 Convolutional layers followed by average pooling followed by 3 ...
Viktor VN's user avatar
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1 vote
1 answer
43 views

Keras CNN early stopping not working as expected with patience parameter in imbalanced datasets

So I want to stop the cnn when a custom (not implemented in keras) logged metric is not improving with a patience of 5 (I chose macro f1 score) and here's what I did: Created a callback to log the ...
giza2001s's user avatar
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0 answers
40 views

Looking for Code to Generate CNN Architecture Visualization from Model Summary [duplicate]

I am looking for code that generates a Convolutional Neural Network (CNN) architecture image from a model summary (similar to the attached image). Thanks for the help!
Hadil .H's user avatar
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1 answer
49 views

CNN training accuracy flatlines

I'm training a CNN from scratch to do tagging of images. And my training is going nowhere. I was hoping someone could help me identify an obvious error. I would like to end up with a network that ...
laslowh's user avatar
  • 101
0 votes
3 answers
152 views

Why Relu is correct for CNN?

Relu only passes positive values, so when we calculate the gradients for this layer, we will only get positive gradients. The gradient for the filter weights of this layer is the convolution of the ...
Tima's user avatar
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0 answers
15 views

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 ...
Mr Squid's user avatar
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0 votes
0 answers
22 views

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 ...
ml_nnoobb's user avatar
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0 answers
60 views

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 ...
Sweta 's user avatar
0 votes
1 answer
149 views

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 ...
mon's user avatar
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0 votes
0 answers
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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 ?
kardev's user avatar
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0 votes
1 answer
128 views

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 ...
Ted Wilmont's user avatar
1 vote
1 answer
213 views

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). ...
Alejandro Graciano's user avatar
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0 answers
31 views

Why does the AutoKeras NAS require reshaping of data?

Please take a look at the following source codes: training.py ...
user366312's user avatar
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0 answers
20 views

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 ...
Clouseau's user avatar
0 votes
1 answer
28 views

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 ...
Hussain Bhavnagarwala's user avatar
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0 answers
318 views

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....
Shagufta's user avatar
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0 answers
30 views

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 ...
Dawood Ahmad's user avatar
0 votes
0 answers
25 views

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 ...
Sebastien's user avatar
1 vote
1 answer
63 views

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 ...
uuR's user avatar
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