I would like to detect and classify an object but in all my training images this object is located at the top of my image. Does Yolo take into account the position on an image for the training and verification?
In other words, if my object is at the bottom of a test image but at the top of all trained images, how does Yolo treat it? Does it decrease the confidence somehow?
Thanks
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I have a set of icons and a screen recording, the icons are not annotated and have no bounding boxes, they are just png icons with image-level labels eg: "instagram", "facebook", "chrome".
The task is to find the icons within the screen recording and draw a bounding box around them, given the above prerequistes.
My idea of approach so far is:
Use selective search to find ROIs
Use a CNN to classify the regions
Filter out non-icons regions
Draw bounding boxes around positive labelled ROIs
Use resulting screen images with bounding boxes to train a FAST R-CNN
but I am stuck at step 2, I have no idea on how to train the CNN with the image-level labelled icons.
If I make a dataset of all the possible icon-images, with no background or context informations, is it possible to train the CNN to answer the question "Does the ROI includes a possible icon?"
I've trained a Kaggle dataset (this one) to detect hand gestures. when training, it gives the val_accuracy = 1.00, here is an image or you can see it using the
link to colab
when I try to test the model using an image from the dataset, it gives right predictions, but when I try to use real-world image for "ok" gesture (you can see it in the end of the colab project), it just gives wrong outputs, I've tries other images, it gives also wrong predictions.
any help please?
When you have a real world image you want to predict you must process that image in exactly the same as you processed the training images. For example
image size must be the same
pixels must be scaled the same
if trained on rgb images real world image must be an rgb image
if trained on grayscale real world image must be gray scale
I am still a beginner in deep learning, I am wondering is it necessary to have for the input images of a size equal to 32*32 (or X*X)? the dimensions of my images are 457*143.
Thank you.
If you want to implement a LeNet and train it from the scratch, you don't have to resize your images. However, if you want to do transfer learning, you'd better resize your images according to the image size of the dataset on which your neural net is already trained.
I am working with object detection using Tensorflow. I have mix of 7-8 classes. Initially, we had an image classification model and now moving it to object detection model. For once class alone, the object to be detected occupies the entire image. Can we have the bounding box dimension to be the entire width and height of the image? Will it hinder the performance?
It shouldn't hinder the performance as long as there's enough such examples in the training set.
the OD API clips detections outbounding the image, so in these cases the resulting bounding box would be the of the entire image (or one axis would be the entire size, and the other less, depending on the object occupation).
Assuming your OD model uses anchors, make sure you have anchors which are responsible for such cases (i.e. with scale of about the entire image).
I am trying to train YOLO on scanned images. The problem is that resizing images to 416x416 distroys the quality of the images.
Is there any way to keep the same size of the images with YOLO ?