I trained a 3D semantic segmentation model and want to test it on some test slices. Should I provide a volumetric test image with the same shape as the input volumetric training image? What is the conventional approach? Is there any way to feed 2D test images and get the predictions?
I tried to feed a volumetric test image of the same spatial dimensions but with fewer slices (17). I was not able to apply the model since it was trained on a volumetric image that had 32 slices.
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I am trying to train my model UNET which segments images. I used random crop on a large image for training. The problem i have is, my images have different size in training and testing. Which method i can use for prediction on large image?
I tried to predict a full image and predict patch by patch with each patch's size correspond image size on training data. But i still don't undertand why i don't have the same result between two methods.
I had taken a drone image of a village and tried to classify using a trained model. The process was successful but it returned a black mask without any contour of bunds traced on it. What could be the reason and solution?enter image description here
I've trained simple convolutional neural network for image classification. it reached 97% accuracy for validation set. my dataset includes images taken by camera from paper book and also screenshot from pdf. but when i want to predict image taken from book by webcam, the model accuracy and confidence is very bad.
these are images:
image taken by mobile, high predict accuracy
very bad result.
for better generalization, I also added random noise to my dataset images but it's not good yet.
Even i face this kind of problem in my computer vision tasks.
i think there is some image quality difference b/w your webcam image and mobile phone image...
what i do is mix both types (can also include images taken from other cameras just to increase the variety) of images in your training data and validation data.
This would generalize your model on any data
I'm trying to make a dataset of images for object detection model, and I don't know if I can train the model in images that condtain only the object and the cordinate will be the full image, and test it on an images that contain multible objects for example:
the training images look like:
train-img
train-img2
test imags
will this effect the localization somehow ?
It really depends on the training images. If your training images (all of them) exactly the ones you uploaded the accuracy of your model on the test images will not be good. In fact in real world scenarios all the training images will not be identical and should have a lot of variation in order for you to generalize your model better.
The Normal Image
The Anomaly Image
I have tried using CNN Autoencoder for anomaly detection. I trained it with just the normal images and then tried to test the model on anomaly images and used reconstruction error to classify the image as an anomaly or not, but the autoencoder is not able to reconstruct the normal images properly.