The paper reports that "having an RoI pooling layer that is differentiable w.r.t the box coordinates is a nontrivial problem" and refers to "ROI Warping" (crops
input_shape=(100,100,6) input_tensor=keras.Input(input_shape) model.add(Conv2D(32, 3, padding='same', activation='relu', input_shape=input_shape))
So I am trying to use a pre-trained model on my data set to then compare it to my own cnn model. However, I see an error as soon as I try to do model. fit so mu
I have a situation where I need to use ImageFolder with the albumentations lib to make the augmentations in pytorch - custom dataloader is not an option. To thi
I am using tensorflow 2.4 and Cuda 11. I've build a CNN model from scratch using tensorflow and frozen it to create the .pb and .pbtxt file.Now I am trying to m
This is the code from https://keras.io/examples/vision/image_classification_from_scratch/ import tensorflow as tf from tensorflow import keras from tensorflo
I have a task for my project paper and I do not get how to train the model. This model is supposed to take an image and segment it into different classes. The h
I want to save a image file to see about difference using convolution layer with dilation rate and without that. Of course I can search images about that, but I
I made a CNN model for training b/w images for training it on TPU on dimension of 100*100. Added the basic callback but after running it, it was giving outputs
I am training a convolutional neural network for binary time series classification. The training accuracy on both models is very different. If on the first it g
If I freeze my base_model with trainable=false, I get strange numbers with trainable_weights. Before freezing my model has 162 trainable_weights. After freezin
If I freeze my base_model with trainable=false, I get strange numbers with trainable_weights. Before freezing my model has 162 trainable_weights. After freezin
I am trying to understand an example snippet that makes use of the PyTorch transposed convolution function, with documentation here, where in the docs the autho
I am training a CNN with an dataset of images that consists of 2410 RGB images and belongs to two categories, i.e., crops and another is grass. After training t
np_utils.to_categorical Keras method give me an error when i gived it a a vector of [962] element which contain 3 classes [1,1,1,...,2,2,2,...3,3,3]. The used
I tried a sample code for CNN application on MNIST data classification from the book : from keras import layers from keras import models model = models.Sequent
I'm trying to make a classification on an image with the CNN algorithm. When I tried to run it on jupyter notebook(anaconda) it didn't work, but when I run it o
Given an output prediction of shape [1,21,388,88] from my Unet. How can I plot it as a masked image? I am using PASCAL dataset. Thanks!
I've created a multi-class image classifier using CNN. I am using the keras module specifically and I am using generators to fit and then predict 4 different cl
Tensorflow/Keras I have developed a CNN model to classify images as circle, triangle or square. However, my accuracy values have wide fluctuations. Is it someth