All Questions
Tagged with convolutional-neural-networks training
58 questions
2
votes
0
answers
29
views
How to analyze furniture for digital reconstruction?
My end-goal is to take a single photo of a piece of common furniture (couch, chair, table) and create a 3d model from that. I'm a novice with deep learning as I've only done basic CNN's and such with ...
- 119
0
votes
0
answers
19
views
My CNN validation Accuracy increases super slow?
im doing a retinopathy detection project with over 3500 images, 700 in each class. I've filtered the image like
It seems that my model isn't learning from the data, or is having trouble because the ...
- 1
0
votes
2
answers
64
views
How do I improve my model accuracy and val_accuracy for my cnn model?
I'm using 3000+ retinopathy images in my CNN model. The accuracy remains around 77 to 80, how do i improve the accuracy value and reduce loss value?
I've tried dropout and Adam optimizer to increase ...
- 1
0
votes
0
answers
46
views
Early divergence of YOLOv7-tiny train and val obj_loss plots
I am training a YOLOv7-tiny model and have the following observations from the training session:
the train and val objectness loss plots diverged pretty early on in the training process
the class and ...
- 1
0
votes
0
answers
18
views
Monitoring validation loss with confident mispredictions
I am working on a binary classification task using a variant of ResNet. The dataset consists of medical recordings and is relatively small (N=2000), though I apply various validated data augmentation ...
- 101
0
votes
0
answers
24
views
What's the advantage of multi-GPU training in Alex-Net?
I was reading ImageNet Classification with Deep Convolutional Neural Networks(Alex et al) and they trained their model on two GPUs following fine-grained structure. Can you tell me why they chose that ...
0
votes
0
answers
22
views
adversarial training on convnext shows a very strange curve
i am currently working on a research project where I have to train some models for adversarial robustness. I have implemented the algorithm used by a research paper called adversarial training for ...
- 1
1
vote
1
answer
639
views
Which epoch is the best for me to choose?
I have trained my deep learning model. I also saved the validation loss to a file and plotted on a graph
I have $2$ questions for this:
Does the validation loss look normal? Is there any issue with ...
user78437
0
votes
1
answer
91
views
YOLOv1 doesn't work for custom dataset
I am currently trying to train my own YOLOv1 network, based on this repository:
https://github.com/ivanwhaf/yolov1-pytorch
The images I want to train look like this:
Three classes I want to detect:
...
- 111
0
votes
0
answers
34
views
Inference time of VGG16 when initialised with different weights
I’m trying to understand the differences in inference time and training time between two models:
VGG16 with weights initialised from a Glorot uniform distribution and the same network with the only ...
1
vote
1
answer
89
views
Why training the same model on the same data can be slower on better card?
Can someone explain why training CNN model (in my case DenseNet201) on the same data, and the same data processing pipeline can be slower on better GPU (RTX3090) than worse one (RTX3060), with the ...
- 1,280
0
votes
0
answers
55
views
Impact of imbalanced dataset on CNN model performance
I trained a 1D CNN model to model bacterial plate count based on time series data of water temperature. Bacterial place count is numerical, based on which I created two category variables, namely &...
- 251
-1
votes
1
answer
81
views
What practically makes a good architecture of ANN?
When we take a look at the literature there are so many opinions.
I was wondering what are some generally good practices to design an architecture, like how much depth would you prefer and how much ...
- 218
0
votes
1
answer
999
views
Validation accuracy less than training accuracy (with no sigh of overtraining)
I am working with a deep CNN with over 100k sample data. I divided it up into 75% training, 12.5% validation and 12.5% for testing. As I train my network, the training accuracy approaches near 100% ...
- 11
0
votes
0
answers
95
views
Feeding the output back to input in 3D CNN model
I am currently designing a Model which takes Input 3D Grid and Model Output at $t-1$. The model figure is described below
I have two thoughts in training the model for above situation.
Feed output $...
- 1
3
votes
2
answers
1k
views
Can some of the weights be fixed during the training of a neural network?
Is it possible to exclude specific layers from the optimization?
For example, let's say I have an input layer, 2 hidden layers, and the output layer. I know there is a perfect solution for my problem ...
- 43
1
vote
1
answer
84
views
A neural network to learn the connection between two totally different type of images
I have a dataset of two different type of images. Say, I have images of a person and his all 10 fingerprints. I want to create a relation between them to predict one from another. How I can do that ...
- 11
2
votes
0
answers
92
views
How to add prior information when predicting using deep learning models?
Background
I'm building a binary classification model for a pair match problem using CNN, e.g. whether person A1 likes product B1 or not. Model input features are sequence features (text descriptions) ...
- 21
1
vote
1
answer
1k
views
How to interpret this learning curve of my neural network?
How to interpret the following learning curves?
Background: The accuracy starts at 50%, because the network has a binary output (0 or 1). I chose an exponentially decreasing learning rate of the ...
-1
votes
2
answers
315
views
Semantic segmentation CNN outputs all zeroes
I'm using MATLAB 2019, Linux, and UNet (a CNN specifically designed for semantic segmentation). I'm training the network to classify all pixels in an image as either cell or background to get ...
1
vote
2
answers
915
views
Why do the training and validation loss curves diverge?
I was training a CNN model on TensorFlow. After a while I came back and saw this loss curve:
The green curve is training loss and the gray one is validation loss. I know that before epoch 394 the ...
3
votes
1
answer
4k
views
Can the (sparse) categorical cross-entropy be greater than one?
I am using AlexNet CNN to classify my dataset which contains 10 classes and 1000 data for each class, with 60-30-10, splits for train, validation, and test. I used different batch sizes, learning ...
- 160
0
votes
1
answer
304
views
Will changing the dimension reduction size of a neural network (i.e. SSD ResNet-50) change the overall outcome and accuracy of the model?
I am training a convolutional neural network to detect objects (weeds amongst crops, in my case) using TensorFlow. The original dimensions of the raw training photos are 4000 x 3000 pixels, which must ...
- 129
2
votes
1
answer
1k
views
Are there any rules for choosing batch size? [duplicate]
I am training a CNN with a batch size of 128, but I have some fluctuations in the validation loss, which are greater than one. I want to increase my batch size to 150 or 200, but, in the code examples ...
- 160
1
vote
0
answers
89
views
How to have closer validation loss and training loss in training a CNN
I am using an AlexNet architecture as my Convolutional Neural Network.
A learning rate of 0.00007 and 128 batch_size.
I have 20000 data and 10% test, 40% validation, and 50% for training.
I used 100 ...
- 160
1
vote
0
answers
146
views
Training a CNN for semantic segmentation of large 4600x4600px images
I am trying to implement a CNN (U-Net) for semantic segmentation of similar large grayscale ~4600x4600px medical images. The area I want to segment is the empty space (gap) between a round object in ...
- 11
1
vote
2
answers
757
views
How to handle images that don’t pertain to image classifier at all?
I am trying to create a CNN model that classifies if a person is wearing a seatbelt or not to verify they drive safely. I know to get images of people wearing seatbelts and people not wearing ...
1
vote
0
answers
161
views
What is the amount of test data needed to evaluate a CNN?
I have an image dataset of about 400 images. 70% of these data points were used for training, 15% for validation, and 15% for testing. I am using the 70% to train a CNN-based binary classifier. I ...
- 11
2
votes
0
answers
46
views
Why is the loss of one of the outputs of a model with multiple outputs increasing while the others are decreasing?
I'm a newbie in neural networks. I'm trying to fit my neural network that has 3 different outputs:
semantic segmentation,
box mask and
box coordinates.
When my model is training, the loss of ...
- 131
1
vote
0
answers
591
views
What is the use of concatenate layer in CNN?
I am not asking what does concatenate layer does in general in point of mathematical operation. But at feature level, what significance does it provide. Does it helps removing false negatives or does ...
- 111
2
votes
1
answer
147
views
How many layers exists in my neural network?
I have a neural network model defined as below. How many layers exist there? Not sure which ones to count when we are asked about the number.
...
- 983
7
votes
3
answers
3k
views
When training a CNN, what are the hyperparameters to tune first?
I am training a convolutional neural network for object detection. Apart from the learning rate, what are the other hyperparameters that I should tune? And in what order of importance? Besides, I read ...
- 71
1
vote
0
answers
26
views
Training dataset for convolutional neural network classification - will images captured on the ground be useful for training aerial imagery?
I am an agronomy graduate student looking to classify crops from weeds using convolutional neural networks (CNNs).
The basic idea that I am wanting to get into involves separating crops from weeds ...
- 129
2
votes
0
answers
73
views
Which CNN hyper-parameters are most sensitive to centered versus off centered data?
Which hyper-parameters of a convolutional neural network are likely to be the most sensitive to depending on whether the training (and test and inference) data involves only accurately centered images ...
- 121
2
votes
1
answer
152
views
Is my fine-tuned model learning anything at all?
I am practicing with Resnet50 fine-tuning for a binary classification task. Here is my code snippet.
...
- 241
1
vote
1
answer
152
views
How to explain peak in training history of a convolutional neural network?
I am training a simple convolutional neural network to recognize two types of 1024-point frequency spectra (FFT). This is the model I'm using:
...
- 249
1
vote
1
answer
41
views
Semantic issues with predictions made by my trained model
I'm new to Deep Learning. I used Keras and trained a inception_resnet_v2 model for my binary classification application (fire ...
- 983
2
votes
1
answer
278
views
Can you build a pure CNN phoneme classification model?
I was making a simple phoneme classification model for a 10 week-long class project and I ran into a small question.
Is it possible to create a model that takes a 1-second (the longest phoneme is 0.2 ...
4
votes
2
answers
2k
views
Wouldn't convolutional neural network models work better without flattening the input in any stages?
The above model is what really helped me understand the implementation of convolutional neural networks, so based on that, I've got a tricky hypothesis that I want to find more about, since actually ...
- 177
3
votes
1
answer
3k
views
How can I reduce the GPU memory usage with large images?
I am trying to train a CNN-LSTM model. The size of my images is 640x640. I have a GTX 1080 ti 11GB. I am using Keras with the TensorFlow backend.
Here is the model.
...
- 33
7
votes
1
answer
220
views
How do neural network topologies affect GPU/TPU acceleration?
I was thinking about different neural network topologies for some applications. However, I am not sure how this would affect the efficiency of hardware acceleration using GPU/TPU/some other chip.
If, ...
- 173
2
votes
1
answer
90
views
How is a neural network where the majority of inputs are 0 trained?
Consider AlexNet, which has 1000 output nodes, each of which classifies an image:
The problem I have been having with training a neural network of similar proportions, is that it does what any ...
- 1,406
4
votes
1
answer
2k
views
How can I incrementally train a Yolo model without catastrophic forgetting?
I have successfully trained a Yolo model to recognize k classes. Now I want to train by adding k+1 class to the pre-trained weights (k classes) without forgetting previous k classes. Ideally, I want ...
- 83
2
votes
0
answers
20
views
Binary annotations on large, heterogenous images
I'm working on a deep learning project and have encountered a problem. The images that I'm using are very large and extremely detailed. They also contain a huge amount of necessary visual information, ...
- 21
2
votes
1
answer
52
views
how to benefit from previous training weights in training again to increase accuracy?
I have trained a modified VGG classification CNN, with random initialized weights; therefor the validation accuracy was not high enough for me to accept (around 66%).
now using the weights resulted ...
- 125
4
votes
1
answer
2k
views
Why are not validation accuracy and loss as smooth as train accuracy and loss?
I am training a modified VGG16 network for classification (adding 0.5 dropout after each of the last FC layers). In the following plot I am training for a small number of epochs as an example, and it ...
- 125
0
votes
1
answer
112
views
CNN output generally has more than one category in one-hot categorization?
I'm a bit of a CNN newbie, and I'm trying to train one to image classify pictures of pretty similar looking particles. I'm making the inputs and labels by hand from a set of 48x48 grayscale images, ...
- 155
3
votes
0
answers
113
views
How to train CNN such it eliminate dependent features and focuses on independent ones?
How we should train a CNN model when training dataset contains only limited number of cases, and the trained model is supposed to predict class (label) for several other cases, which has not seen ...
- 131
5
votes
2
answers
4k
views
Is pooling a kind of dropout?
If I got well the idea of dropout, it allows improving the sparsity of the information that comes from one layer to another by setting some weights to zero.
On the other hand, pooling, let's say max-...
- 258
0
votes
1
answer
421
views
Is 1mb an acceptable memory size for images being trained in a CNN?
I am using Tensorflow CNN to build an image classification/prediction model. Currently all the images in the dataset are each about 1mb in size.
Most examples out there use very small images.
The ...
- 103