My training prototxt file uses images of size 400x400. I dont use any cropping, image resizing in the training. I am finetuning the Alexnet for my problem.
In my deploy.protxt file I define image size as 350x350.
I am using matcaffe for training as well as testing.
I expect an error message as I use different image sizes in the train.prototxs and deploy.prototxt. However I didnt get any error messages, the matlab code works fine.
Since this contains a fully connected layer, the image sizes have to be same. In that case why I didnt get any errors?
May be this is due to the strides used? 400x400 and 350x350 images will give the same size of the feature maps at the layer before the fully connected one?
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I've implemented the original UNet-Architecture from its paper. It works with 572x572 images and predicts 388x388 images (original paper is without padding). I have used this network for another task which has 2048x1024 images as input to create the same sized (2048x1024) target. This fails, because the image size doesnt agree with the network architecture. So I saw on github a code which sets padding = 1 for all convolutions and everything works. Fine.
My question: Is that a common thing? "Randomly (maybe experimentally is better)" tweakin padding or stride parameters until the dimensions fit? But then it isn't the original UNet anymore, right?
I am glad for any advices, because I want to learn a good way for using existing networks in different challenges.
Best
I have images of around 2000 X 2000 pixels. The objects that I am trying to identify are of smaller sizes (typically around 100 X 100 pixels), but there are lot of them.
I don't want to resize the input images, apply object detection and rescale the output back to the original size. The reason for this is I have very few images to work with and I would prefer cropping (which would lead to multiple training instances per image) over resizing to smaller size (this would give me 1 input image per original image).
Is there a sophisticated way or cropping and reassembling images for object detection, especially at the time of inference on test images?
For training, I suppose I would just take out the random crops, and use those for training. But for testing, I want to know if there is a specific way of cropping the test image, applying object detection and combining the results back to get the output for the original large image.
I guess using several (I've never tried) networks simultaneously is a choice, for you, using 4*4 (500+50 * 500+50) with respect to each 1*1), then reassembling at the output stage, (probably with NMS at the border since you mentioned the target is dense).
But it's weird.
You know one insight in detection with high resolution images is altering the backbone with "U" shape shortcut, which solves some problems without resize the images. Refer U-Net.
I'm working on a deep learning project and have encountered a problem. The images that I'm using are very large and extremely detailed. They also contain a huge amount of necessary visual information, so it's hard to downgrade the resolution. I've gotten around this by slicing my images into 'tiles,' with resolution 512 x 512. There are several thousand tiles for each image.
Here's the problem—the annotations are binary and the images are heterogenous. Thus, an annotation can be applied to a tile of the image that has no impact on the actual classification. How can I lessen the impact of tiles that are 'improperly' labeled.
One thought is to cluster the tiles with something like a t-SNE plot and compare the ratio of the binary annotations for different regions (or 'classes'). I could then assign weights to images based on where it's located and then use that as an extra layer in my training. Very new to all of this, so wouldn't be surprised if that's an awful idea! Just thought I'd take a stab.
For background, I'm using transfer learning on Inception v3.
I'm using the AWS SageMaker "built in" object detection algorithm (SSD) and we've trained it on a series of annotated 512x512 images (image_shape=512). We've deployed an endpoint and when using it for prediction we're getting mixed results.
If the image we use for prediciton is around that 512x512 size we're getting great accuracy and good results. If the image is significantly larger (e.g. 8000x10000) we get either wildly inaccurate, or no results. If I manually resize those large images to 512x512pixels the features we're looking for are no longer discernable to the eye. Which suggests that if my endpoint is resizing images, then that would explain why the model is struggling.
Note: Although the size in pexels is large, my images are basically line drawings on a white background. They have very little color and large patches of solid white, so they compress very well. I'm mot running into the 6Mb request size limit.
So, my questions are:
Does training the model at image_shape=512 mean my prediction images should also be that same size?
Is there a generally accepted method for doing object detection on very large images? I can envisage how I might chop the image into smaller tiles then feed each tile to my model, but if there's something "out of the box" that will do it for me, then that'd save some effort.
Your understanding is correct. The endpoint resizes images based on the parameter image_shape. To answer your questions:
As long as the scale of objects (i.e., expansion of pixels) in the resized images are similar between training and prediction data, the trained model should work.
Cropping is one option. Another method is to train separate models for large and small images as David suggested.
I am trying to train my model which classifies images.
The problem I have is, they have different sizes. how should i format my images/or model architecture ?
You didn't say what architecture you're talking about. Since you said you want to classify images, I'm assuming it's a partly convolutional, partly fully connected network like AlexNet, GoogLeNet, etc. In general, the answer to your question depends on the network type you are working with.
If, for example, your network only contains convolutional units - that is to say, does not contain fully connected layers - it can be invariant to the input image's size. Such a network could process the input images and in turn return another image ("convolutional all the way"); you would have to make sure that the output matches what you expect, since you have to determine the loss in some way, of course.
If you are using fully connected units though, you're up for trouble: Here you have a fixed number of learned weights your network has to work with, so varying inputs would require a varying number of weights - and that's not possible.
If that is your problem, here's some things you can do:
Don't care about squashing the images. A network might learn to make sense of the content anyway; does scale and perspective mean anything to the content anyway?
Center-crop the images to a specific size. If you fear you're losing data, do multiple crops and use these to augment your input data, so that the original image will be split into N different images of correct size.
Pad the images with a solid color to a squared size, then resize.
Do a combination of that.
The padding option might introduce an additional error source to the network's prediction, as the network might (read: likely will) be biased to images that contain such a padded border.
If you need some ideas, have a look at the Images section of the TensorFlow documentation, there's pieces like resize_image_with_crop_or_pad that take away the bigger work.
As for just don't caring about squashing, here's a piece of the preprocessing pipeline of the famous Inception network:
# This resizing operation may distort the images because the aspect
# ratio is not respected. We select a resize method in a round robin
# fashion based on the thread number.
# Note that ResizeMethod contains 4 enumerated resizing methods.
# We select only 1 case for fast_mode bilinear.
num_resize_cases = 1 if fast_mode else 4
distorted_image = apply_with_random_selector(
distorted_image,
lambda x, method: tf.image.resize_images(x, [height, width], method=method),
num_cases=num_resize_cases)
They're totally aware of it and do it anyway.
Depending on how far you want or need to go, there actually is a paper here called Spatial Pyramid Pooling in Deep Convolution Networks for Visual Recognition that handles inputs of arbitrary sizes by processing them in a very special way.
Try making a spatial pyramid pooling layer. Then put it after your last convolution layer so that the FC layers always get constant dimensional vectors as input . During training , train the images from the entire dataset using a particular image size for one epoch . Then for the next epoch , switch to a different image size and continue training .