enter image description herei have 3000 images for both training and testing in one folder and i also have the image label in label.csv file which has the five class categories. Can anyone help me how to split this dataset into train and test data so that i can classify the images using convolution neural network. My dataset looks like the following image after the linking with csv and images.
First, you need an association between images and labels (some kind of knowledge of which label belongs to which image). Otherwise it will not work properly. After that you can split your dataset. Here is a toy example, assuming full_dataset contains the whole dataset and SIZE_OF_DATASET is the size of full_dataset:
full_dataset = full_dataset.shuffle()
train_dataset = full_dataset.take(int(0.8*SIZE_OF_DATASET))
test_dataset = full_dataset.skip(int(0.2*SIZE_OF_DATASET))
Related
I'm new to machine learning, and i have been working on a project for early dementia detection using cnn.
I am facing issue in oversampling my data.(data is MRI images from imported from kaggle with train and test classes having 4 sub classes(nondemented,milddemented....)). the train data has around 5120 images and test has around 1200 with 176258 size which i have resized to 176176
for x,y in train_data:
images.append(x)
images = np.concatenate(images)
train_images = images.reshape(len(images),176*176*3)
sm=SMOTE(sampling_strategy='minority',random_state=42)
train_images=sm.fit_resample(train_images)
this is the code,i have applied the same procedure for test data as well upto reshaping, in the last line its causing an error, now i know there in fit_resample there has to be 2 arguments second one been labels, but in this case where i just have images, what should i put there as second argument, should it be my test_data? i have no clue. please help me
I am new to machine learning and want to ask a question about the "flow_from_directory" function in Keras.
I have trained an image recognition model with ResNet50, and now I want to predict the test images with this model. There are 5 classes of images, which are "daisy", "dandelion", "rose", "sunflower", and "tulip", and their label are corresponding to [10000],[01000],[00100],[00010],and[00001] respectively.
Attach is part of my code to read and predict the test images:
The variable "filenames" is a list of the test images, and the variable "y_act" should be the actual labels of the test images.
However, I found the sequence of "filenames" doesn't match with the "y_act", see the two attached images:
I want to make the "filenames" and the "y_act" in the same sequence, does anyone knows how do realize this? Thanks a lot in advance
I'm using CNN model for Image Classification. My dataset has a folder and 5 sub-folders that has images of flowers in it. I have created the data frame where feature column has the path of each image and output column has the label in the form of dummy variable. I want to split this data frame for training the CNN model. Here, the dataset is imbalance. So, I have to use Stratified K-fold. But, I'm not getting how to do it. Can anyone please help.?
Here is the image of my code.
enter image description here
enter image description here
You can use tf.image.decode_jpeg(tf.io.read_file(path),channels=3) even use tfDataset.
I have a dataset of images that have two folders: test and training. I need to do object detection using OpenCV and Yolo.
Thus, I need to create my own Yolo model for the street objects.
For the training folder:
training
Example training image:
training image
For the test folder:
test
I have the classes txt file which includes id, name and classification (warning, indication and mandatory).
Example:
0 = animal crossing (warning)
1 = soft verges (warning)
2 = road narrows (warning)
Here, the numbers are the numbers (or ids) in the training folder, names, and classification.
My purpose is to create a Yolo model from these training images. I have checked some papers and articles, but in their case, they label the full image using labelimg, but in my case training images are so small and they don't need any labeling.
Thus, I'm confused about how to do this. Could you please give me some ideas?
Labeling images is a must in YOLO's that's how they deal with their loss functions. To detect objects something called (intersection over union )
More easy way to label images is by using (roboflow site ).
I would refer to this image that describes the different types of computer vision tasks.
I think what you want to do is a Classification tasks. Yolo is for Object Detection tasks, where you usually want to detect more than one object per image.
For classification tasks, it can be easier because you don't need to make separate label files. The names of the folders are the labels. Here is an example of a classification model that you can use https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
If you really want to use Yolo you will need to make label files. If you are going to do Classification of the whole image then the format of the annotation will be easy. It would be something like this.
`0 0.5 0.5 1 1' The first column is the class number: 0,1,2,3 etc. You will need to make one file for each image with the name .txt.
Does this help you?
Iam new to keras and after testing some tutorials with mnist images I would like to train with my own data set. The data are .png images of numbers from 0-9.
I ordered them into 10 classes, each containing 100 .png images of the numbers separately (so one folder for 0, one folder for 1, one folder for 2 etc ..).
now I am wondering how to load the images with python, for keras to use them ?
You need to use Keras’ ImageDataGenerator().flow_from_directory() to generate batches of your image data from your file system that you will then train your model on. Once you have your images organized in the file system, creating ImageDataGenerator() would be the next step.
This video demonstrates how to prep your image data and create your ImageDataGenerator(), and then this video demonstrates how to train your CNN on the image data.
An example of this would look like
train_batches = ImageDataGenerator().flow_from_directory(directory=<path_to_image_data>, target_size=(224,224), classes=[‘0’, '1', ‘2’, ‘3’, …, ‘9’], batch_size=10)