I have been working on a dataset in which the goal is to determine that which type of orientation is it. It is a classification problem in which for each record(for most of them) I am having 4 images - front facing, left facing, right facing and back facing product images.
I want to classify these images in the above 4 categories.
The dataset looks like this :
I have downloaded the images and put them in different folders according to their classes.
Methods I have applied:
Till now I have applied two methods to classify these images.
1) I have tried vgg16 directly to classify the images but it did not give me even 50% accuracy.
2) I converted those images into edge images with black background as:
This is done using canny edge detection. It was done because in the result I was getting images with similar color dresses, similar design dresses, etc.
On top of these I again applied vgg16, resnet50, inception models but nothing seemed to work.
Can you suggest some ideas that can work in my case and classify the images in a better way.
first of all yor data set has to be equally splited. For instance 80% train and 20% test. After that you have to balance these sets (train set 60% of class A images, 40% of class B images) the exact same for test set.
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How can I modify the example in the "Object Detection FasterRCNN Tutorial" (https://www.kaggle.com/code/pdochannel/object-detection-fasterrcnn-tutorial) to include empty labeled images in the training data? I want to be able to handle cases where there are no classes of interest in certain images.
Additionally, I have noticed that even when I include images of underwater scenes (where there are no objects of interest) the model still tends to detect something.
I have 5 folders (which represent 5 classes, and each contain about 200 colored images), I want to use "Principal Component Analysis" for image classification.
previously I used Resnet to predict to which class each image belong. but now I want to use the PCA.
I am trying to apply that with code, any help please?
previously I used Resnet to predict to which class each image belong. but now I want to use the PCA.
PCA is not a method for classification. It is a dimensional reduction method that is sometimes used as a processing step.
Take a look at this CrossValidated post for some more explanation. It has an example.
(FYI, saw this because you pinged me via the MATLAB Answers forum.)
I am trying to implement an image classification task for the grayscale images, which were converted from some sensor readings. It means that I had initially time series data e.g. acceleration or displacement, then I transformed them into images. Before I do the transformation, I did apply normalization across the data. I have a 1000x9 image dimension where 1000 represents the total time step and 9 is the number of data points. The split ratio is 70%, 15%, and 15% for training, validation, and test data sets. There are 10 different labels, each label has 100 images, it's a multi-class classification task.
An example of my array before image conversion is:
As you see above, the precisions are so sensitive. When I convert them into images, I am able to see the darkness and white part of the image;
Imagine that I have a directory from D1 to D9 (damaged cases) and UN (health case) and there are so many images like this.
Then, I have a CNN-network where my goal is to make a classification. But, there is a significant overfitting issue and whatever I do it's not working out. One of the architecture I've been working on;
Model summary;
I also augment the data. After 250 epochs, this is what I get;
So, what I wonder is that I tried to apply some regularization or augmentation but they do not give me kind of solid results. I experimented it by changing the number of hidden units, layers, etc. Would you think that I need to fully change my architecture? I basically consider two blocks of CNN and FC layers at the end. This is not the first time I've been working on images like this, but I cannot mitigate this overfitting issue. I appreciate it if any of you give me some solid suggestions so I can get smooth results. i was thinking to use some pre-trained models for transfer learning but the image dimension causes some problems, do you know if I can use any of those pre-trained models with 1000x9 image dimension? I know there are some overfiting topics in the forum, but since those images are coming from numerical arrays and I could not make it work, I wanted to create a new title. Thank you!
I work on a project of classification of images (in 5 types).
The images have various sizes, so I reshape them first and then use a CNN.
I have already pretty good results. However, the type of my images is very correlated with their shape (for example image of type A are very little and image of type B is very big, in 99% of case).
So, I think that my score would significatively increase if I would give to my model the information on the original size of the image. But I don't know how do this... How can I give this information to my model? (I work with Keras)
Thanks in advance,
Larapa
I have been thinking about building a YOLO model for detecting parking lot occupancy, I have all the small segmented out images for every parking space. Can I train YOLO on these small images already divided into separate empty and occupied classes and test it on a test image like the ariel view of a parking lot with say 28 parking spots and the model should detect the occupied and empty spaces.
If yes then can someone guide me how to approach the problem? I will be using YOLO implemented on Keras.
YOLO is a n object detection model. During training, it takes coordinates of bounding boxes in an image as input and learns to identify the images inside such bounding boxes. As per your problem statement, if you have a aerial view of parking lot then draw the bounding boxes, generate xml files (as per your training requirement) and start training. This ideally should give you the desired model to predict.
Free tool to label images - https://github.com/tzutalin/labelImg
Github project to get an idea of how to train Yolo in Keras on custom dataset - https://github.com/experiencor/keras-yolo2
By any means, this is not a perfect tailor made solution for your problem given you haven't provided any code or images. But this is a good place to start.